Large Number Of Attributes Research Articles

Combining Hybrid Approach Redefinition-Multiclass Imbalance (HAR-MI) and Hybrid Sampling in Handling Multi-Class Imbalance and Overlapping

The class imbalance problem in the multi-class dataset is more challenging to manage than the problem in the two classes and this problem is more complicated if accompanied by overlapping. One method that has proven reliable in dealing with this problem is the Hybrid Approach Redefinition-Multiclass Imbalance (HAR-MI) method which is classified as a hybrid approach that combines sampling and classifier ensembles. However, in terms of diversity among classifiers, a hybrid approach that combines sampling and classifier ensembles will give better results. HAR-MI provides excellent results in handling multi-class imbalances. The HAR-MI method uses SMOTE to increase the number of samples in the minority class. However, this SMOTE also has a weakness where an extremely imbalanced dataset and a large number of attributes will be over-fitting. To overcome the problem of over-fitting, the Hybrid Sampling method was proposed. HAR-MI combination with Hybrid Sampling is done to increase the number of samples in the minority class and at the same time reduce the number of noise samples in the majority class. The preprocessing stages at HAR-MI will use the Minimizing Overlapping Selection under Hybrid Sampling (MOSHS) method, and the processing stages will use Different Contribution Sampling. The results obtained will be compared with the results using Neighbourhood-based under-sampling. Overlapping and Classifier Performance will be measured using Augmented R-Value, the Matthews Correlation Coefficient (MCC), Precision, Recall, and F-Value. The results showed that HAR-MI with Hybrid Sampling gave better results in terms of Augmented R-Value, Precision, Recall, and F-Value

Generic Feasibility Assessment: Helping to Choose the Nuclear Piece of the Net Zero Jigsaw

The United Kingdom has declared a climate change policy of 100% reduction in carbon dioxide emissions by 2050. Efforts thus far have been limited solely to electricity generation methods. While progress has been admirable, effort now must be directed at the nation’s non-electrical energy use. Nuclear energy is an essential part of any energy future, since it is low-carbon, firm and supplies synchronous electricity; however the nation’s nuclear strategy to date has been erratic, costly and lacking in strategic oversight. A multitude of reactor designs are on offer for potential uptake, and decision-makers must have clarity of vision on what these systems must deliver before forming a strategy. Choosing between these systems, given the uncharted energy future faced by the UK is a daunting prospect. Generic feasibility assessment offers a tool for decision-makers to assist them in selecting the most suitable nuclear system for chosen future conditions. Generic feasibility assessment offers an alternative to traditional multi-attribute decision analyses, which can be confusing to even committed stakeholders when large numbers of attributes are weighted and compiled. Generic feasibility assessment forms part of a toolkit which will be of utility in achieving net zero by 2050, given the short time that remains.

RETRACTED: Mixed-mode database miner classifier: Parallel computation of graphical processing unit mining

Graphical processing unit (GPU) has gained more popularity among researchers in the field of decision making and knowledge discovery systems. However, most of the earlier studies have GPU memory utilization, computational time, and accuracy limitations. The main contribution of this paper is to present a novel algorithm called the Mixed Mode Database Miner (MMDBM) classifier by implementing multithreading concepts on a large number of attributes. The proposed method use the quick sort algorithm in GPU parallel computing to overcome the state of the art limitations. This method applies the dynamic rule generation approach for constructing the decision tree based on the predicted rules. Moreover, the implementation results are compared with both SLIQ and MMDBM using Java and GPU with the computed acceleration ratio time using the BP dataset. The primary objective of this work is to improve the performance with less processing time. The results are also analyzed using various threads in GPU mining using eight different datasets of UCI Machine learning repository. The proposed MMDBM algorithm have been validated on these chosen eight different dataset with accuracy of 91.3% in diabetes, 89.1% in breast cancer, 96.6% in iris, 89.9% in labor, 95.4% in vote, 89.5% in credit card, 78.7% in supermarket and 78.7% in BP, and simultaneously, it also takes less computational time for given datasets. The outcome of this work will be beneficial for the research community to develop more effective multi thread based GPU solution in GPU mining to handle large set of data in minimal processing time. Therefore, this can be considered a more reliable and precise method for GPU computing.

Feature selection with ensemble learning for prostate cancer diagnosis from microarray gene expression.

Cancer diagnosis using machine learning algorithms is one of the main topics of research in computer-based medical science. Prostate cancer is considered one of the reasons that are leading to deaths worldwide. Data analysis of gene expression from microarray using machine learning and soft computing algorithms is a useful tool for detecting prostate cancer in medical diagnosis. Even though traditional machine learning methods have been successfully applied for detecting prostate cancer, the large number of attributes with a small sample size of microarray data is still a challenge that limits their ability for effective medical diagnosis. Selecting a subset of relevant features from all features and choosing an appropriate machine learning method can exploit the information of microarray data to improve the accuracy rate of detection. In this paper, we propose to use a correlation feature selection (CFS) method with random committee (RC) ensemble learning to detect prostate cancer from microarray data of gene expression. A set of experiments are conducted on a public benchmark dataset using 10-fold cross-validation technique to evaluate the proposed approach. The experimental results revealed that the proposed approach attains 95.098% accuracy, which is higher than related work methods on the same dataset.

General Learning Equilibrium Optimizer: A New Feature Selection Method for Biological Data Classification

ABSTRACT Finding relevant information from biological data is a critical issue for the study of disease diagnosis, especially when an enormous number of biological features are involved. Intentionally, the feature selection can be an imperative preprocessing step before the classification stage. Equilibrium optimizer (EO) is a recently established metaheuristic algorithm inspired by the principle of dynamic source and sink models when measuring the equilibrium states. In this research, a new variant of EO called general learning equilibrium optimizer (GLEO) is proposed as a wrapper feature selection method. This approach adopts a general learning strategy to help the particles to evade the local areas and improve the capability of finding promising regions. The proposed GLEO aims to identify a subset of informative biological features among a large number of attributes. The performance of the GLEO algorithm is validated on 16 biological datasets, where nine of them represent high dimensionality with a smaller number of instances. The results obtained show the excellent performance of GLEO in terms of fitness value, accuracy, and feature size in comparison with other metaheuristic algorithms.

The Role of Context Definition in Choice Experiments: a Methodological Proposal Based on Customized Scenarios

One of the most critical points for the validity of Discrete Choice Experiments lies in their capability to render the experiment as close to actual market conditions as possible. In particular, when dealing with products characterized by a large number of attributes, the construction of the experiment poses the issue of how to express the choice question providing sufficient information. Our study verifies the role of scenario definition in choice experiments and proposes a methodology to build customized scenarios by eliciting responses from interviewees on the main choice criteria, which makes it possible to render the conditions of the experiment more realistic. This methodology is applied to the case study of wine and is introduced by a systematic review of the Discrete Choice Experiments conducted on wine. The findings show that customized scenarios result in different preference estimates compared to the conventional approach. In particular, we found a significant decline in the importance of the price attribute, which could be attributed to a better definition of the product being evaluated. Moreover, the methodology is capable of gathering information on the decision-making process that would otherwise remain unobserved and that can be used for a better segmentation analysis.

Comparative Experiments for Classification of Diabetes Mellitus

Diabetes is an affecting people disease nowadays. Over 246 million people worldwide with a majority of them being women had been affected by diabetes. According to the World Health Organization (WHO), by 2025, this number is expected to increase to over 380 million. The disease has been ranked as the fifth deadliest disease in United States with no imminent cure in sight. Along with the increasing of the information technology and its continued advent into the medical and healthcare sector, the cases of this disease and the symptoms are well documented. This paper aims at finding the best performance by four classification algorithms, which are Naive Bayes, Simple Logistics, REPTree, and Sequential Minimal Optimization (SMO). Model testing on 200 tuples with 9 attributes in the diabetes dataset revealed that Naive Bayes achieved highest accuracy of 85%. To improve the overall accuracy, it is necessary to use more data set with larger number of attributes and use a better feature selection method in future works.

Generalized objects in the system with dispersed knowledge

The inference process based on a set of local decision tables is considered in the article. Determining global decisions in such a situation is a complex and time-consuming task. The aim of the presented approach is to simplify this process by significantly reducing the size of local decision tables and, at the same time, maintaining the quality of decisions made. The research objective is the application of generalized objects with respect to the indiscernibility relation. When we use the generalized objects only the relevant and consistent knowledge remains in tables. In addition, the use of generalized objects for local tables results in a significant reduction in the number of objects in the tables. Such a change has a huge impact on the system’s construction for dispersed data.The paper introduces a definition of the generalized objects that are suitable for quantitative data. In addition, a definition of the generalized objects generated with the accuracy expressed by the parameter, which are appropriate for qualitative data, is given.It was experimentally tested that the use of generalized objects significantly reduces the number of objects in local tables, up to 80% or even 90% percent of the original table. In addition, it was shown that the use of generalized objects for local tables with large number of attributes gives comparable quality of classification to the dispersed system using full objects. Moreover, it was depicted that the quality of classification obtained for such data and the dispersed system with the generalized objects is better than the quality that are obtained for full objects without using the dispersed system.

Gaussian process regression for seismic fragility assessment of building portfolios

Seismic fragility assessment of building portfolios is often based on the analysis of “average” building models representative of structural types (or building classes), thus neglecting building-to-building variability within a structural type. This paper proposes the use of Gaussian process (GP) regressions to develop flexible and accurate metamodels explicitly mapping building-class attributes to the seismic fragility parameters. The proposed metamodels can enable analysts to account for building-to-building variability in simulation-based seismic risk assessment of building portfolios. Unlike other commonly-used metamodels, GP regressions do not require the a-priori definition of a prediction function and they quantify the uncertainty on the predictions in a refined and explicit fashion. The proposed method is demonstrated for a portfolio of seismically-deficient reinforced concrete school buildings with construction details typical of some developing countries. Based on the available information about the building attributes (e.g. geometry, materials, detailing), building realisations are generated based on two alternative approaches, which are critically compared: design of experiment and Monte Carlo sampling. Cloud-based time-history analysis for each building realisation is performed using unscaled real ground-motion records; fragility relationships are derived for four structure-specific damage states. A GP regression is then developed for each considered fragility parameter (i.e. median and dispersion). To further increase the tractability and scalability of the methodology, alternative metamodels are defined based on numerical non-linear static pushover analyses or analytical “by-hand” pushover analyses, through the Simple Lateral Mechanism Analysis (SLaMA) method. The results show that, for the considered portfolio, the fitted GP regressions have a high predictive power in surrogating the modelled fragility, demonstrating the feasibility of the approach in practice. It is also shown that the choice of the sampling technique could be based on the input data availability, rather than on the expected computational burden. Finally, the use of simplified methods for response analysis shows acceptable error levels with respect to the full time-history analysis results. Such simplified methods can be promising alternatives to generate large training datasets for the proposed GP regressions. This increases the potential of training metamodels in practical portfolio risk assessment applications, in which a high number of building types, each characterised by a large number of attributes, is generally involved.

A non-specialized ensemble classifier using multi-objective optimization

Ensemble classification algorithms are often designed for data with certain properties, such as imbalanced class labels, a large number of attributes, or continuous data. While high-performing, these algorithms sacrifice performance when applied to data outside the targeted domain. We propose a non-specific ensemble classification algorithm that uses multi-objective optimization instead of relying on heuristics and fragile user-defined parameters. Only two user-defined parameters are included, with both being found to have large windows of values that produce statistically indistinguishable results, indicating the low level of expertise required from the user to achieve good results. Additionally, when given a large initial set of trained base-classifiers, we demonstrate that a multi-objective genetic algorithm aiming to optimize prediction accuracy and diversity will prefer particular types of classifiers over others. The total number of chosen classifiers is also surprisingly small – only 10.14 classifiers on average, out of an initial pool of 900. This occurs without any explicit preference for small ensembles of classifiers. Even with these small ensembles, significantly lower empirical classification error is achieved compared to the current state-of-the-art.

A Novel Approach in Determining Neural Networks Architecture to Classify Data With Large Number of Attributes

One of the challenges in the successful implementation of deep neural networks (DNN) lies on the determination of its architecture, in terms of the number of hidden layers and neurons for each hidden layer. In this research, a new approach is proposed to determine the neural networks architecture especially in the form of Multi-layer Perceptron (MLP) which will later be used as a machine learning method to classify data with large number of attribute. The new approach is proposed since the previous approaches are no longer applicable as general guidelines to determine the architecture of neural networks. Thus, the proposed approach aims to determine the number of hidden layers by using principal component analysis (PCA), while the number of neurons for each hidden layer is determined by using K-Means clustering. The determined neural network architecture is utilized to classify data with large number of attribute, such as the Gas Sensor Array Drift dataset which has 128 input attributes and six output classes and the Parkinson's Disease Classification dataset which has 754 output attributes and two output classes. The results indicate that the best-performing architecture for the first dataset is the one that uses one hidden layer, with a PCA cumulative variance of 69.7%, while for the second dataset is the one that uses three hidden layers, with a PCA cumulative variance of 38.9%. Increasing the number of hidden layers does not always improve the performance of neural networks. Therefore, it is essential to determine the number of hidden layers and neurons that are appropriate to achieve good performance in neural networks. The use of PCA and K-Means clustering is expected to provide guidelines in determining neural networks architectures with good performance.

Decision support system for determining the activities of the study program using the Preference Selection Index

Each year the study program carries out activities that have been planned through departmental meetings. However, study program activities that are not on target will result in less accreditation scores. Accreditation is a government effort to guarantee the quality of educational institutions. The value of accreditation determines the main assets in the field of education, the quality of study programs and the feasibility of graduates. Therefore, this study tries to provide a ranking formula for study program activities based on the assessment of study program accreditation forms. The system is built using one of type multi-criteria decision making (MCDM), namely the Preference Selection Index (PSI). PSI method using statistical concepts until preference index as a reference for alternative ranking. This developed system can help the department to recommend the main activities that can be done to support the efforts of the study program in achieving optimal accreditation values. Finally, it was concluded that the PSI method is the right method for problems that require decision support with a large number of attributes, such as the criteria used in recommendations for study program activities that have been adapted to the study program accreditation form assessment matrix.

Multivariate Top-Coding for Statistical Disclosure Limitation.

One of the most challenging problems for national statistical agencies is how to release to the public microdata sets with a large number of attributes while keeping the disclosure risk of sensitive information of data subjects under control. When statistical agencies alter microdata in order to limit the disclosure risk, they need to take into account relationships between the variables to produce a good quality public data set. Hence, Statistical Disclosure Limitation (SDL) methods should not be univariate (treating each variable independently of others), but preferably multivariate, that is, handling several variables at the same time. Statistical agencies are often concerned about disclosure risk associated with the extreme values of numerical variables. Thus, such observations are often top or bottom-coded in the public use files. Top-coding consists of the substitution of extreme observations of the numerical variable by a threshold, for example, by the 99th percentile of the corresponding variable. Bottom coding is defined similarly but applies to the values in the lower tail of the distribution. We argue that a univariate form of top/bottom-coding may not offer adequate protection for some subpopulations which are different in terms of a top-coded variable from other subpopulations or the whole population. In this paper, we propose a multivariate form of top-coding based on clustering the variables into groups according to some metric of closeness between the variables and then forming the rules for the multivariate top-codes using techniques of Association Rule Mining within the clusters of variables obtained on the previous step. Bottom-coding procedures can be defined in a similar way. We illustrate our method on a genuine multivariate data set of realistic size.

Self Organising Fuzzy Logic Classifier for Predicting Type-2 Diabetes Mellitus using ACO-ANN

In today’s digital world, a dataset with large number of attributes has a curse of dimensionality where the computation time grows exponentially with the number of dimensions. To overcome the problem of computation time and space, appropriate method of feature selection can be developed using metaheuristic approaches. The aim of this work is to investigate the use of ant colony optimization with the help of neural network to select near optimal feature subset and integrate it with the self-organizing fuzzy logic classifier for improving the recognition rate. The proposed fuzzy classifier derives prototype from the collected data through an offline training process and uses it to develop a fuzzy inference system for classification. Once trained, it can continuously learn from streaming data and later adapts the changing facts by updating the system structure recursively. The developed model is not based on predefined parameters used in the data generation model but is derived from the empirically observed data.

Seleksi Fitur Berbasis Pearson Correlation Untuk Optimasi Opinion Mining Review Pelanggan

The comments contained on e-commerce users generally contain opinions about positive or negative experiences at several online shops. Sentences that can be written indirectly both a little or a lot, will affect other potential customers. So as a result of these comments cause a product sold at an online store has a rating of two things namely "recommended" or "non-recommended". However, detection of positive and negative opinions manually will require more time because of the large amount of data. For this reason opinion mining using technology in data mining can be used to automate positive and negative detection of comments. However, one of the main problems in opinion mining is limited data but has a large number of attributes. In this study, we propose the application of Pearson correlation (PC) based feature selection for opinion mining optimization. The results of the experiment show that the application of PC increases the performance of opinion mining systems in 3 types of classification, namely Logistic Regression, Naïve Bayes and Support Vector Machine, resulting in more optimal accuracy, namely 98.80%, 87.87% and 98.12%.

Integrating machine learning algorithms with OLAP systems for enhanced predictive analytics

Combining ordinary ML algorithms with the extraordinary technology of OLAP creates a novel way of improving the accuracy of predictive models. The multidimensional analysis used in OLAP systems is useful for processing large amount of data and the modernization through the ML algorithms in Decision making systems offers useful prediction technique. This study examines the implementation of OLAP with ML algorithms, including decision trees, neural networks, and regression to enhance the predictive and real-time data capability. It is extended by integrating ML features into an OLAP system and its performance is evaluated on a large-scale BI data set from a similar BI application. The approach includes tuning OLAP system, choosing right set of ML algorithms, as well as designing an integration approach to achieve high prediction accuracy with reasonable computational cost. Findings indicate that the use of ML in conjunction with OLAP leads to better predictive performance and better scalability, including with respect to dealing with large numbers of attributes and query types. The paper also shows the business intelligence advantages of this approach, including the more precise identification of trends, risks, and opportunities. Moreover, this research highlights potential problems, including data compatibility and system performance, for future study in this area.

Hierarchical Encoder with Auxiliary Supervision for Neural Table-to-Text Generation: Learning Better Representation for Tables

Generating natural language descriptions for the structured tables which consist of multiple attribute-value tuples is a convenient way to help people to understand the tables. Most neural table-to-text models are based on the encoder-decoder framework. However, it is hard for a vanilla encoder to learn the accurate semantic representation of a complex table. The challenges are two-fold: firstly, the table-to-text datasets often contain large number of attributes across different domains, thus it is hard for the encoder to incorporate these heterogeneous resources. Secondly, the single encoder also has difficulties in modeling the complex attribute-value structure of the tables. To this end, we first propose a two-level hierarchical encoder with coarse-to-fine attention to handle the attribute-value structure of the tables. Furthermore, to capture the accurate semantic representations of the tables, we propose 3 joint tasks apart from the prime encoder-decoder learning, namely auxiliary sequence labeling task, text autoencoder and multi-labeling classification, as the auxiliary supervisions for the table encoder. We test our models on the widely used dataset WIKIBIO which contains Wikipedia infoboxes and related descriptions. The dataset contains complex tables as well as large number of attributes across different domains. We achieve the state-of-the-art performance on both automatic and human evaluation metrics.

Single-valued neutrosophic context analysis at distinct multi-granulation

In the current era, most of the researchers addressed an issue while dealing with uncertainty and indeterminacy that exists in fuzzy attributes. It becomes more complex when indeterminacy exists independently when compared to acceptation and rejection part. Due to which, some of the researchers tried to develop a three-way fuzzy concept lattice using neutrosophic set for characterization of uncertainty based on its acceptation, rejection, and uncertain parts, independently. In this process, a problem was addressed while processing the neutrosophic context based on user-required subset of attributes. It takes more time to extract interesting pattern from a given neutrosophic context having a large number of attributes. One of the solutions is to decompose the neutrosophic context via a defined multi-granulation for the truth, falsity and indeterminacy, membership values. To accomplish this task, a method is proposed in this paper using the computing paradigm of granular computing and applied lattice theory with an example.

Case-base maintenance of a personalised and adaptive CBR bolus insulin recommender system for type 1 diabetes

People with type 1 diabetes must control their blood glucose level through insulin infusion either with several daily injections or with an insulin pump. However, estimating the required insulin dose is not easy. Recommender systems, mainly based on Case-Based Reasoning (CBR), are being developed to provide recommendations to users. These systems are designed to keep the experiences or cases of the user in a case-base, which requires maintenance to keep system’s response accurate and efficient. This paper proposes a case-base maintenance methodology that combines case-base redundancy reduction and attribute weight learning. Contrary to previous approaches designed for classification problems, the maintenance methodology presented in this paper deals with numerical recommendations. It can manage a potentially huge case-base due to the combinatorial derived from the number of attributes used to represent a case. The proposed approach has been tested using the UVA/PADOVA type 1 diabetes simulator and the results demonstrate that it can accomplish better levels of accuracy than other insulin recommender systems mentioned in the literature, when a large number of attributes is considered.

Hybrid microaggregation for privacy preserving data mining

k-Anonymity by microaggregation is one of the most commonly used anonymization techniques. This success is owe to the achievement of a worth of interest trade-off between information loss and identity disclosure risk. However, this method may have some drawbacks. On the disclosure limitation side, there is a lack of protection against attribute disclosure. On the data utility side, dealing with a real datasets is a challenging task to achieve. Indeed, the latter are characterized by their large number of attributes and the presence of noisy data, such that outliers or, even, data with missing values. Generating an anonymous individual data useful for data mining tasks, while decreasing the influence of noisy data is a compelling task to achieve. In this paper, we introduce a new microaggregation method, called HM-pfsom, based on fuzzy possibilistic clustering. Our proposed method operates through an hybrid manner. This means that the anonymization process is applied per block of similar data. Thus, we can help to decrease the information loss during the anonymization process. The HM-pfsom approach proposes to study the distribution of confidential attributes within each sub-dataset. Then, according to the latter distribution, the privacy parameter k is determined, in such a way to preserve the diversity of confidential attributes within the anonymized microdata. This allows to decrease the disclosure risk of confidential information.