University Of California , Irvine Research Articles

A new hybrid ensemble feature selection framework for machine learning-based phishing detection system

This paper proposes a new feature selection framework for machine learning-based phishing detection system, called the Hybrid Ensemble Feature Selection (HEFS). In the first phase of HEFS, a novel Cumulative Distribution Function gradient (CDF-g) algorithm is exploited to produce primary feature subsets, which are then fed into a data perturbation ensemble to yield secondary feature subsets. The second phase derives a set of baseline features from the secondary feature subsets by using a function perturbation ensemble. The overall experimental results suggest that HEFS performs best when it is integrated with Random Forest classifier, where the baseline features correctly distinguish 94.6% of phishing and legitimate websites using only 20.8% of the original features. In another experiment, the baseline features (10 in total) utilised on Random Forest outperforms the set of all features (48 in total) used on SVM, Naive Bayes, C4.5, JRip, and PART classifiers. HEFS also shows promising results when benchmarked using another well-known phishing dataset from the University of California Irvine (UCI) repository. Hence, the HEFS is a highly desirable and practical feature selection technique for machine learning-based phishing detection systems.

Defending harassers harms victims

The Letter “Harassment charges: Injustice done?” by A. Moya et al. (17 August, p. [655][1]) defends Francisco Ayala, recasting him as the victim of an unfair investigation where he was found guilty of repeated sexual harassment against multiple colleagues, leading to his resignation from the University of California Irvine (UCI). In publishing this Letter, the content of which is considered retaliatory under U.S. education law ([ 1 ][2]), Science legitimizes attempts to discredit Ayala's accusers by reinforcing the narrative that consequences are the “fault” of the harassed rather than those engaging in harassment. Moya et al. do not substantiate their claims that the UCI investigation was “enacted in haste” and that it lacked “fairness” and “due process.” Instead, they propose that Ayala's status and research accomplishments should shield him from the consequences of his actions. As addressed in the Letter “Editor's note: Harassment policy” (12 October, p. [165][3]) by Science's Editor-in-Chief, Jeremy Berg, “[s]uch arguments not only lack relevance to harassment behavior but also may result in further harm to the targets of harassment and exacerbate the already daunting process that targets face in coming forward publicly.” Moya et al. also decry the “appalling consequences” of the charges against Ayala, deflecting attention from the consequences of his proven misconduct and impugning the motives of those who testified. Approximately 50% of women face sexual harassment in academic workplaces—more so for women of color and sexual and gender minorities ([ 2 ][4]). Many who report also face retaliation ([ 2 ][4]). Ayala's targets were courageous in coming forward. They deserve support, not public criticism enabled by a prestigious scientific organization. Science would not publish such a letter defending a data fabricator on the basis of their accomplishments and making unsupported claims against the investigation. Although Science has since changed its policy, the scientific community should reflect on why the standard was different for sexual misconduct and act accordingly to mitigate damages. Sexual harassment undermines core values of research integrity. It not only harms targets and bystanders; harassment damages science itself by discouraging and driving out talented people. In our opinion, Science committed an injustice and a serious error in judgment by publishing this Letter. We request that Science retract the Moya et al. letter or remove it from the online archive, and publicly apologize to both Ayala's victims and the scientific community at large ([ 3 ][5]). Editor's note We thank Barolo et al. for continuing the dialogue about best practices for addressing harassment. Barolo et al. call for the retraction or removal of the Letter by Moya et al . Science originally published the Letter to fulfill the Letters section's mission of representing diverse views within the community. Since publication, Science has acknowledged that some of the points in the Moya et al. Letter were flawed. However, because Letters represent the opinions of the authors and not editorial positions of Science , we will not retract previously published Letters. Furthermore, in line with current best practice, Science policy precludes removing text once it is published. Rather, before the Letter itself, we have prominently placed the text of the 12 October 2018 Editor's Note, which alerts readers to concerns about the Letter and explains new policies put in place since its publication. Jeremy Berg Editor-in-Chief Published 25 January 2019 1. [↵][6]1. S. M. Galanter , “Dear colleague letter” (U.S. Department of Education, Washington, DC, 2013); . 2. [↵][7]National Academies of Sciences, Engineering, and Medicine, “Sexual harassment of women: Climate, culture, and consequences in academic sciences, engineering, and medicine” (The National Academies Press, Washington, DC, 2018); [www.nap.edu/catalog/24994/sexual-harassment-of-women-climate-culture-and-consequences-in-academic][8]. 3. [↵][9]A petition preceding this Letter and 316 online signatories are available at [www.gopetition.com/petitions/the-evidence-does-not-support-amplifying-sympathy-for-harassers.html][10]. [1]: http://www.sciencemag.org/content/361/6403/655.3 [2]: #ref-1 [3]: http://www.sciencemag.org/content/362/6411/165.1 [4]: #ref-2 [5]: #ref-3 [6]: #xref-ref-1-1 View reference 1 in text [7]: #xref-ref-2-1 View reference 2 in text [8]: http://www.nap.edu/catalog/24994/sexual-harassment-of-women-climate-culture-and-consequences-in-academic [9]: #xref-ref-3-1 View reference 3 in text [10]: http://www.gopetition.com/petitions/the-evidence-does-not-support-amplifying-sympathy-for-harassers.html

Accuracy Rejection Normalized-Cost Curves (ARNCCs): A Novel 3-Dimensional Framework for Robust Classification

Machine learning (ML) offers several supervised learning algorithms to build classifiers for developing accurate decision support systems. However, the selection of robust classifier for reliable decision making in healthcare domain is three-fold: accuracy, refraining for low confidence decisions, and the cost of decisions. In the field of medical science, there are costs associated with the incorrect and the refraining from decisions, which can have negative implications in devising adequate therapeutic interventions. For example, it may be life threating if a cancer patient is declared as healthy one (misclassification cost) or decision remains pending for some time (rejection cost). In this work we proposed the concept of Accuracy Rejection Normalized-Cost Curves (ARNCCs), which is an extension of Accuracy Rejection Curves (ARCs); a three-dimensional visualization technique to demonstrate the strengths and weaknesses of classification algorithms over different rejection regions and normalized-cost (NC) to select the robust classifier. ARNCCs method holds ARCs plot on two dimensions, in addition it computes NC at third dimension against ratio of false positive costs and ratio of rejection costs obtained at different rejection rates. The proposed three-dimensional graphs have the potential to answer a variety of questions regarding accuracy, rejection rate and NC of a classifier. Six publicly available cancer datasets (four breast cancer and two lung cancer) having clinical parameters obtained from ML repository of University of California, Irvine (UCI) were used to assess the performance of proposed ARNCCs in this study. Empirical results show that ARNCCs provide broad range of decisions to choose the desired parameters (accuracy, rejection rate and NC) for further necessary actions as compared to traditional ARCs method. ARNCCs framework has the ability to more logically compare the performances of classification algorithms in terms of accuracy, rejection rate and NC based scenarios.

New Associative Classification Method Based on Rule Pruning for Classification of Datasets

In data mining, a rule-based classification approach called Associative Classification (AC) normally builds accurate classifiers from supervised learning data sets. It extracts “If-Then” rules and associates each of the generated rules with two computed parameters; support and confidence. These two parameters are utilized to differentiate the rules’ superiority during the building of a classifier’s step. In current AC algorithms, whenever a rule is inserted into a classifier, all of its corresponding training data is discarded. However, the discarded data actually are used to compute support and confidence of other rules and will affect other lower ranked rules since rules normally have common training data examples. The use of static support and confidence may result in very large less-accurate classifiers.Thus, a procedure that amends other rules’ support and confidence is important. This paper proposes a new procedure named Active Pruning Rules (APR) to overcome the above problem so then the classifiers’ performance - especially predictive accuracy and reducing rule redundancy - will be further improved. The experimental results obtained from a number of University of California Irvine (UCI) data sets and real adult autism classification data set showed that APR is highly competitive to other AC and rule-based classifiers and often produces smaller yet more predictive classifiers.

A roller bearing fault diagnosis method using interval support vector deterministic optimization based on nested PSO

An interval support vector deterministic optimization model (ISVD) is proposed for the fault classification problem of uncertainty samples in this paper. Firstly, based on the order relation theory, support vector machines using interval samples is transformed into ISVD, which is a two-objective optimization problem, with the midpoint and the radius optimized at the same time. Then, ISVD is converted into a single objective optimization model by the linear combination. The single objective optimization model includes Lagrange multiplier vector and interval sample vectors, both of which are nested. Thus, the nested particle swarm optimization (PSO) based on dynamic decreasing inertia weight is applied to select the optimal Lagrange multiplier vector of this model. Lastly, the effectiveness of the proposed method is proved by the data set of University of California Irvine (UCI) and the roller bearing fault experiments. The experimental results show: ISVD owns outstanding generalization ability with the help of the structured risk minimization and global optimization. The accuracy of the proposed ISVD is better than that of the native Bayes uncertain classification method 1 (NBU1), native Bayes uncertain classification method 2 (NBU2) and the formula-based Bayes classifier (FBC).

An Improved Skewness Decision Tree SVM Algorithm for the Classification of Steel Cord Conveyor Belt Defects

Skewness Decision Tree Support Vector Machine (SDTSVM) algorithm is widely known as a supervised learning model for multi-class classification problems. However, the classification accuracy of the SDTSVM algorithm depends on the perfect selection of its parameters and the classification order. Therefore, an improved SDTSVM (ISDTSVM) algorithm is proposed in order to improve the classification accuracy of steel cord conveyor belt defects. In the proposed model, the classification order is determined by the sum of the Euclidean distances between multi-class sample centers and the parameters are optimized by the inertia weight Particle Swarm Optimization (PSO) algorithm. In order to verify the effectiveness of the ISDTSVM algorithm with different feature space, experiments were conducted on multiple UCI (University of California Irvine) data sets and steel cord conveyor belt defects using the proposed ISDTSVM algorithm and the conventional SDTSVM algorithm respectively. The average classification accuracies of five-fold cross-validation were obtained, based on two kinds of kernel functions respectively. For the Vowel, Zoo, and Wine data sets of the UCI data sets, as well as the steel cord conveyor belt defects, the ISDTSVM algorithm improved the classification accuracy by 3%, 3%, 1% and 4% respectively, compared to the SDTSVM algorithm. The classification accuracy of the radial basis function kernel were higher than the polynomial kernel. The results indicated that the proposed ISDTSVM algorithm improved the classification accuracy significantly, compared to the conventional SDTSVM algorithm.

BSSReduce an <inline-formula> <tex-math notation="LaTeX">$O(\left|U\right|)$</tex-math> </inline-formula> Incremental Feature Selection Approach for Large-Scale and High-Dimensional Data

With the advent of the era of big data, data has become bigger than ever. Recently, as a fundamental task of pattern recognition, predict and data mining, feature selection has aroused wide public concern. However, extant methods on feature selection have an $O(\left|C\right|^x\left|U\right|^y)$ time complexity, which is the bottleneck preventing people from exploring knowledge in large-scale or high-dimensional datasets. Based on bijective soft sets, we propose a new rationale for feature selection, which can help break that bottleneck. Subsequently, this paper proposes an $O(\left|U\right|)$ feature-selection method whose computational time increases linearly only with the number of instances. To validate the proposed method, we conduct extensive experiments on the University of California Irvine (UCI) datasets in which large-scale and high-dimensional datasets containing four million instances and over three million features are included. The results reveal that the proposed method is an efficient, effective, and outperforms traditional methods in runtime, which can save massive computing resources. Moreover, the proposed method can be applied to feature selection for large-scale and gigantic-dimensional datasets, which are difficult to process with traditional methods.

Artificial immune system based wastewater parameter estimation

The basis of a wastewater treatment system is to achieve the desired characteristics of the wastewater treatment process. An estimation of the obtained wastewater treatment characteristics provides the information needed to set up the current process steps, and it is important to have an optimum treatment. In this study, an artificial immune system (AIS) structure is developed to estimate important wastewater output parameters such as pH, DBO, DQO, and SS for the first time. The proposed AIS models are based on the clonal selection principle, and the dataset is provided from the University of California Irvine (UCI) Machine Learning Library. The current dataset is analyzed by principal component analysis (PCA) to obtain maximum system performance. As a result of the simulation, the output parameters are successfully predicted using the AIS model with real data. The classifier's performance ratios are studied separately using the coefficient of determination (R$^{2})$ and the mean squared error of prediction (MSEP), and their rates are given in this study.

Fuzzy Clustering with the Structural α‐Entropy

We study fuzzy clustering with the structural α-entropy and present a unified framework for fuzzy clustering with fuzzy entropy, which can be regarded fuzzy clustering with fuzzy entropy as its special case. Then, aiming at weighting exponent m equal to the structural α-entropy index α in the presented unified framework, we obtain the fuzzy membership degrees and cluster centers using Lagrange method. Further, we propose the Structural α-entropy based fuzzy c-means (SEFCM) algorithm. Moreover, to solve clustering of the complicated data, we also present the Structural α-entropy based kernel fuzzy c-means (SEKFCM) algorithm. In experiment, some University of California Irvine (UCI) data sets and synthetic data sets are used to test the performance of the presented algorithms and the role of the structural α-entropy. The experimental results show that the presented algorithms obtain better clustering result.

Attribute-oriented cognitive concept learning strategy: a multi-level method

Recently, formal concept analysis has become a potential direction of cognitive computing, which can describe the processes of cognitive concept learning. We establish a concept hierarchy structure based on the existing cognitive concept learning methods. However, none of these methods could obtain the following results: get the concept, recognize objects and distinguish between two different objects. In this paper, our focus is to construct an attribute-oriented multi-level cognitive concept learning method so as to improve and enhance the ability of cognitive concept learning. Firstly, the view point of human cognition is discussed from the multi-level approach, and then the mechanism of attribute-oriented cognitive concept learning is investigated. Through some defined special attributes, we propose a corresponding structure of attribute-oriented multi-level cognitive concept learning from an interdisciplinary viewpoint. It is a combination of philosophy and psychology of human cognition. Moreover, to make the presented attribute-oriented multi-level method easier to understand and apply in practice, an algorithm of cognitive concept learning is established. Furthermore, a case study about how to recognize the real-world animals is studied to use the proposed method and theory. Finally, in order to solve conceptual cognition problems, we perform an experimental evaluation on five data sets downloaded from the University of California-Irvine (UCI) databases. And then we provide a comparative analysis with the existing $$granular\ computing\ approach\ to\ two$$ - $$way\ learning$$ [44] and the three- $$way\ cognitive\ concept\ learning\ via\ multi$$ -granularity [9]. We obtain more number of concepts than $$the\ two$$ - $$way\ learning\ and\ the\ three$$ - $$way\ cognitive\ concept\ learning\ approaches$$ , which shows the feasibility and effectiveness of our attribute-oriented multi-level cognitive learning method.

Improved Adaptive Neuro-Fuzzy Inference System Using Gray Wolf Optimization: A Case Study in Predicting Biochar Yield

Abstract This paper presents an alternative method for predicting biochar yields from biomass thermochemical processes. As biochar is considered a renewable and sustainable energy source, it has received more attention. Several methods have been presented to predict biochar, such as neural network (NN) and least square support vector machine (LS-SVM). However, each of them has its own drawbacks, such as getting stuck in a local optimum, which occurs in NN, and lack of uncertainty and time complexity, as in LS-SVM. Therefore, this paper avoids this limitation by using a hybrid method between the adaptive neuro-fuzzy inference system (ANFIS) and gray wolf optimization (GWO) algorithm. The proposed method is called ANFIS-GWO, which consists of two stages. In the first stage, GWO is used to learn the parameters of ANFIS using the training set. Meanwhile, in the second stage, the testing set is used to evaluate the performance of the proposed ANFIS-GWO method. Three experiments were performed to assess the performance of the proposed method. The first experiment used a set of UCI (University of California, Irvine) benchmark datasets to evaluate the effectiveness of ANFIS-GWO. The aim of the second experiment was to evaluate the performance of the proposed ANFIS-GWO method to predict biochar yield from manure pyrolysis. The third experiment aimed to estimate the values of input parameters of pyrolysis that maximize biochar production. The obtained results were compared to those of other methods, such as ANFIS using gradient descent, practical swarm optimization, genetic algorithm, whale optimization algorithm, sine-cosine algorithm, and LS-SVM. The results of the ANFIS-GWO method were >35% of the standard ANFIS and also better than those of other methods.

Knowledge acquisition and decision making based on Bayes risk minimization method

There are two central parts in multiple attribute decision making (MADM), which are weight assignment and attribute selection. However, attribute selection is usually ignored in the existing researches, which will result in the difficulty of knowledge acquisition and the error of decision making. In addition, with respect to the data set with labels, the existing methods of weight assignment usually neglect or do not take full advantage of the supervisory function of labels, which may also lead to some decision making mistakes. To make up for these deficiencies, this paper proposes a method for knowledge acquisition and decision making based on Bayes risk minimization. In this method, a novel Bayes risk model based on neighborhood and Gaussian kernel is raised, and a heuristic forward greedy algorithm is designed for attribute selection. Finally, a number of experiments, including the comparison experiments on University of California Irvine (UCI) data and the effectiveness evaluation of fighter, are carried out to illustrate the superiority and applicability of the proposed method.

Performance evaluation of supervised learning algorithms with various training data sizes and missing attributes

Supervised learning is a machine learning technique used for creating a data prediction model. This article focuses on finding high performance supervised learning algorithms with varied training data sizes, varied number of attributes, and time spent on prediction. This studied evaluated seven algorithms, Boosting, Random Forest, Bagging, Naive Bayes, K-Nearest Neighbours (K-NN), Decision Tree, and Support Vector Machine (SVM), on seven data sets that are the standard benchmark from University of California, Irvine (UCI) with two evaluation metrics and experimental settings of various training data sizes and missing key attributes. Our findings reveal that Bagging, Random Forest, and SVM are overall the three most accurate algorithms. However, when presence of key attribute values is of concern, K-NN is recommended as its performance is affected the least. Alternatively, when training data sizes may be not large enough, Naive Bayes is preferable since it is the most insensitive algorithm to training data sizes. The algorithms are characterized on a two-dimension chart based on prediction performance and computation time. This chart is expected to guide a novice user to choose an appropriate method for his/her demand. Based on this chart, in general, Bagging and Random Forest are the two most recommended algorithms because of their high performance and speed.

Feature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Network.

Wearable inertial measurement unit (IMU) sensors are powerful enablers for acquisition of motion data. Specifically, in human activity recognition (HAR), IMU sensor data collected from human motion are categorically combined to formulate datasets that can be used for learning human activities. However, successful learning of human activities from motion data involves the design and use of proper feature representations of IMU sensor data and suitable classifiers. Furthermore, the scarcity of labelled data is an impeding factor in the process of understanding the performance capabilities of data-driven learning models. To tackle these challenges, two primary contributions are in this article: first; by using raw IMU sensor data, a spectrogram-based feature extraction approach is proposed. Second, an ensemble of data augmentations in feature space is proposed to take care of the data scarcity problem. Performance tests were conducted on a deep long term short term memory (LSTM) neural network architecture to explore the influence of feature representations and the augmentations on activity recognition accuracy. The proposed feature extraction approach combined with the data augmentation ensemble produces state-of-the-art accuracy results in HAR. A performance evaluation of each augmentation approach is performed to show the influence on classification accuracy. Finally, in addition to using our own dataset, the proposed data augmentation technique is evaluated against the University of California, Irvine (UCI) public online HAR dataset and yields state-of-the-art accuracy results at various learning rates.

Theory and Numerical Analysis of Extreme Learning Machine and Its Application for Different Degrees of Defect Recognition of Hoisting Wire Rope

An improved classification approach is proposed to solve the hot research problem of some complex multiclassification samples based on extreme learning machine (ELM). ELM was proposed based on the single-hidden layer feed-forward neural network (SLFNN). ELM is characterized by the easier parameter selection rules, the faster converge speed, the less human intervention, and so on. In order to further improve the classification precision of ELM, an improved generation method of the network structure of ELM is developed by dynamically adjusting the number of hidden nodes. The number change of the hidden nodes can serve as the computational updated step length of the ELM algorithm. In this paper, the improved algorithm can be called the variable step incremental extreme learning machine (VSI-ELM). In order to verify the effect of the hidden layer nodes on the performance of ELM, an open-source machine learning database (University of California, Irvine (UCI)) is provided by the performance test data sets. The regression and classification experiments are used to study the performance of the VSI-ELM model, respectively. The experimental results show that the VSI-ELM algorithm is valid. The classification of different degrees of broken wires is now still a problem in the nondestructive testing of hoisting wire rope. The magnetic flux leakage (MFL) method of wire rope is an efficient nondestructive method which plays an important role in safety evaluation. Identifying the proposed VSI-ELM model is effective and reliable for actually applying data, and it is used to identify the classification problem of different types of samples from MFL signals. The final experimental results show that the VSI-ELM algorithm is of faster classification speed and higher classification accuracy of different broken wires.

Harassment charges: Enough himpathy

![Figure][1] UCI decided to remove Francisco Ayala's name from the science library after he resigned amid sexual harassment allegations. PHOTO: IAN PARKER We are well into the #Metoo era, yet journalists and editors are still fixated on the harasser's fall from grace rather than the detrimental effect of sexual harassment on the victims and our society as a whole. The News story “Prominent geneticist out at UC Irvine after harassment finding” (M. Wadman, 29 June, [https://scim.ag/AyalaResignation][2]) reinforces a familiar toxic narrative: The accomplishments of the harasser hold more value to science than women's right to a safe workplace. This is now so commonplace that it has been dubbed “himpathy” ([ 1 ][3]). In the News story, Wadman tells us all about the “eminent” professor, from his scientific accomplishments to his personal hobbies. He did “pioneering” and “groundbreaking” work, he donated money to the university, and he was president of AAAS (the publisher of Science ). However, we do not hear about the pioneering work of the women he harassed at University of California, Irvine (UCI). From a graduate student to a tenured professor to an assistant dean, the News story reduced the women who demanded an end to his misconduct to complainers. We are told that Ayala was just being “European” and his actions were misunderstood; instead, the narrative should focus on the many women and careers that suffered from Ayala's actions. Wadman then chose to end the article by quoting an Ayala supporter who diminished the investigation. The same himpathy sentiments return in the follow-up News In Depth story “Report details harassment by famed biologist” (M. Wadman, 27 July, p. [316][4]). Words matter, and Science should wield its words and influence carefully. It is time to recognize that harassers have taken a substantial toll on the advancement of science. It is time to acknowledge that sexual harassment in all its nefarious forms puts an unquantifiable burden on the victims (many of whom are our colleagues). It's time to believe women. 1. [↵][5]1. K. Manne , Down Girl (Oxford Univ. Press, 2017). [1]: pending:yes [2]: http://scim.ag/AyalaResignation [3]: #ref-1 [4]: /lookup/doi/10.1126/science.361.6400.316 [5]: #xref-ref-1-1 View reference 1 in text

Dynamics of an integrated solar photovoltaic and battery storage nanogrid for electric vehicle charging

In this paper, the performance of a renewable Solar Photovoltaic (PV) nanogrid — here defined as a small-scale power system, which comprises a single domain for control, reliability, and power quality — is assessed for Electric Vehicle (EV) charging. A nanogrid testbed, containing PV as the power supply, twenty EV charging stations, a Battery Energy Storage System (BESS), and a smart-inverter is connected to a primary feeder on the University of California, Irvine (UCI) Microgrid. We present four different smart-inverter control algorithms that govern battery dispatch for different energy management goals. The control algorithms were evaluated with respect to four figures of merit: (1) Renewable Penetration, (2) Voltage Profiles, (3) Net Power Flows, and (4) PV curtailed. A steady-state power flow model of the nanogrid was developed to study the local power quality. The control algorithms were able to successfully use the battery to shift the nanogrid peak load and also limit the nanogrid demand to a given threshold. It is shown that the nanogrid was able to offset the EV daily charging loads completely. In this same mode, the renewable contribution to EV charging was 80%, and the overall solar penetration (accounting for export and battery charge) was 89%.

Distance-based double-quantitative rough fuzzy sets with logic operations

Based on various requirements, many generalized rough set models have been developed to alleviate the limitations of generic Pawlak rough set theory and tackle different categories of information systems. One of the limitations is that rough set models based on equivalence relation are only applicable to discrete data information systems, and not suitable for dealing with real-valued continuous data without any prior processing. Another limitation is that “classical” rough sets do not consider the quantitative information about the degree of overlap between equivalence classes and the basic set, so they cannot cope well with the quantification problems. In this paper, we propose a framework of distance-based double-quantitative rough fuzzy set (Db-Dq-RFS) with logic operation by forming a distance-based fuzzy similarity relation in an information system with continuous data to simultaneously solve the two limitations. It is presented how to construct the distance-based fuzzy similarity relation in a normalized information system, and how to use this fuzzy similarity relation to generate distance-based single-quantitative rough fuzzy set (Db-Sq-RFS) models and the Db-Dq-RFS models with logic operation. The proposed Db-Dq-RFS models can overcome certain limitations of the classical rough set model. Following further studies to discuss the decision rules with parameters variation in the four kinds of Db-Dq-RFS models, we present an illustrative example to interpret the proposed developments and to verify the effect of parameters variation on decision rules. To illustrate the effectiveness of the parameters variation on decision rules, experimental evaluation is performed using five datasets coming from the University of California–Irvine (UCI) repository.

Production prediction and energy-saving model based on Extreme Learning Machine integrated ISM-AHP: Application in complex chemical processes

One of the key issues of the sustainable development in all countries is industrial productivity improvement, especially the production capacity improvement and energy-saving of complex chemical processes. Therefore, this paper proposes a production prediction and energy-saving model based on Extreme Learning Machine (ELM) integrated Interpretative Structural Modeling (ISM) and Analytic Hierarchy Process (AHP). The factors that affect the productivity are divided into different levers by the ISM. And then the attributes of each layer are fused by the AHP based on the entropy weight, which greatly reduces the complexity of the input attributes. Moreover, the production prediction and energy-saving model is established based on the ELM. Compared with the traditional ELM, the validity and the practicability of the proposed method are verified by University of California Irvine (UCI) datasets. Finally, the proposed method is applied in the production capacity prediction and energy-saving of ethylene production systems and Purified Terephthalic Acid (PTA) production systems. The experimental results show the proposed method could reduce the number of hidden layer nodes and improve the training time of the ELM. Furthermore, the prediction accuracy of the ethylene production and the PTA production reaches about 99% to improve the energy efficiency of complex chemical processes.

A geodesic distance‐based approach for shape‐independent data clustering using coalitional game

AbstractDifferent clustering approaches consider different aspects of quality including accuracy of cluster formation, speed of execution, and resource requirements. A major issue in data clustering is to consider the density of clusters and their structure. A challenge is to relax any assumption on the clusters shape such as sphericity, which is common in most partitioning approaches such as K‐means. In this article, with the help of the coalitional game theory and the concept of geodesic distance calculation, a density‐based shape‐independent clustering approach is proposed. In addition to the emphasis on the application of game theory, we also pay attention to the relative neighbourhood of data, which is depicted using geodesic distance. Geodesic distance is a well‐known measure for finding the manifold of data. The new idea supposes to discover any embedded structure of data and avoid finding only spherical clusters. The proposed approach is evaluated on a number of standard University of California, Irvine (UCI) datasets, and the results show the effectiveness of the proposed approach in comparison with some other approaches.