Rough Set Algorithm Research Articles

A Novel Analytical Approach Using Rough Set and Genetic Algorithm of a Stable Sensorless Induction Motor Drives in the Regenerating Mode

A novel approach for optimized observer feedback gains of a stable sensorless induction motor (IM) drives at very low speeds in the regenerating mode is presented. The proposed approach depends on the rough set (RS) and genetic algorithm (GA) in a cascading construction. The RS is used to obtain the most dominant machine parameters that affect the stability of the sensorless IM drive at very low speeds in the regenerating mode. The parameter's values are randomly selected to investigate their influence on the stability. Then, a reduction is obtained for the most dominant machine parameters affecting the stability. GA is applied to search for the optimal design of the observer feedback gains under the dominant parameter deviation. The proposed RS theory and GA guarantees a stable speed estimate and efficient sensorless IM drive at very low speeds in the regenerating mode. Theoretical analysis, design procedure, and simulation work of the proposed approach are presented. A sensorless IM drive is executed in the laboratory using the digital signal processor (DSP)-DS1104 control board. Extensive results in the different operating conditions to verify the efficacy of the proposed approach are presented and compared with previous works.

Research on economic system based on fuzzy set comprehensive evaluation model

The development of the economic system is affected by many factors, and the stability of the traditional economic analysis model is difficult to maintain. In order to explore the efficient and stable economic system evaluation and analysis model, based on machine learning ideas, this study uses rough set algorithm as the basic algorithm, and applies the related methods of rough set and catastrophe model theory to the evaluation of ecological economic development level. Moreover, this study reduces the redundant index of the index system and calculates the importance of the index after reduction. Based on the catastrophe set model, this study uses MATLAB software programming to comprehensively quantify the ecological economy, and finally divides the ecological economic grade. In addition, this study combines rough set theory with fuzzy mathematics, and initially establishes a two-branch fuzzy evaluation model. Finally, this study combines the actual situation to use the established model to evaluate the regional eco-economic system. The research results show that the method proposed in this paper has a certain effect, which can provide a reference for subsequent related research.

Improved Approach Based on Fuzzy Rough Set and Sine-Cosine Algorithm: A Case Study on Prediction of Osteoporosis

In recent years, osteoporosis prediction has been paid more attention among healthcare experts and the public. It is a silent disease that causes many fractures and complications that impact the quality of human life; therefore, predicting osteoporosis is important to reduce the risk of fractures; however, many irrelevant descriptors can influence the prediction of osteoporosis, thus, computational methods are needed. In this article, we present a new method to predict osteoporosis, which starts by pre-processing the data to avoid an imbalanced dataset. Then, the sine-cosine algorithm, based on the information gain fuzzy-rough set, is applied to select the most discriminative descriptors. Finally, classifiers are used to predict the deficiency of osteoporosis samples based on the selected descriptors. To evaluate the efficacy of the proposed approach, two experiments were performed using benchmark datasets and real osteoporosis data. The results of the experiments show that the proposed approach achieved competitive results compared to the other methods in selecting the most appropriate descriptors for predicting osteoporosis. The selected descriptors show a high correlation with osteoporosis.

Distributed Feature Selection for Big Data Using Fuzzy Rough Sets

Fuzzy rough-set-based feature selection is an important technique for big data analysis. However, the classic fuzzy rough set algorithm takes all the data correlations into account, which leads to the centralized computing mode, requiring high computing and memory space resources. With the increasing amount of data in the big data era, the centralized server cannot afford the computation of fuzzy rough set. To enable the fuzzy rough set for big data analysis, in this article, we propose the novel distributed fuzzy rough set (DFRS)-based feature selection, which separates and assigns the tasks to multiple nodes for parallel computing. The key challenge is to maintain the global information on each distributed node without conserving the entire fuzzy relation matrix. We tackle this challenge by a dynamic data decomposition algorithm and a data summarization process on each distributed node. Extensive experiments based on multiple real datasets demonstrate that DFRS significantly improves the runtime, and its feature selection accuracy is nearly the same as the traditional centralized computing.

ANALISA DATA EKSPEDISI PAKET DENGAN MENERAPKAN ALGORITMA ROUGH SET (STUDI KASUS: JNE AGEN MENTENG MEDAN)

In analyzing data with data mining using the Rough Set algorithm which has results in the form of rules (rules). The process of determining the rules in the Rough Set method starts from processing the Microsoft Excel database, and continues testing with the Rough Set method. Thus producing General Rules which will become new knowledge in this research.With the existence of data mining using the rough set penilus algorithm, it can analyze data and provide solutions to the weaknesses faced in the menteng Medan field agents that have occurred so that it can be used as an alternative to solving problems in packet expedition encountered so far.Keyword: Data Mining, Descision System, Equivalen Class, ,General Rules, Rough set,Reduction

Predicting Crude Oil Price Using Fuzzy Rough Set and Bio-Inspired Negative Selection Algorithm

The need to accurately predict and make right decisions regarding crude oil price motivates the proposition of an alternative algorithmic method based on real-valued negative selection with variable-sized detectors (V-Detectors), by incorporating with fuzzy-rough set feature selection (FRFS) for predicting the most appropriate choices. The objective of this study is enhancing the performance of V-Detectors using FRFS for prices of crude oil. Applying FRFS serves to prune the number of features by retaining the most informative and critical features. The V-Detectors then trains and tests the features. Different radius values are applied for V-Detectors. Experimental outcome in comparison with established algorithms such as support vector machine, naïve bayes, multi-layer perceptron, J48, non-nested generalized exemplars, IBk, fuzzy-roughNN, and vaguely quantified nearest neighbor demonstrates that FRFS-V-Detectors is proficient and valuable for insightful knowledge on crude oil price. Thus, it can assist in establishing oil price market policies on the international scale.

Analysis of medical rescue strategies based on a rough set and genetic algorithm: A disaster classification perspective

Quantitative research on disaster medical rescue from the perspective of disaster classification can provide a more accurate decision-making foundation for disaster medical rescue management. In this study, the medical features caused by various types of sudden disasters are utilized as the starting point to construct a disaster medical rescue decision table based on the rough set theory. Then, a genetic algorithm is used to analyze the common points of various disaster medical features upon which many disasters are classified. The common features and personality features of disaster medical rescue operations are explored, and systematic recommendations are proposed for medical emergency rescue management based on these features of disaster classification. These results provide theoretical support for the design of disaster relief classification standards, actual plans, and rescue work.

A knowledge resources fusion method based on rough set theory for quality prediction

Quality prediction of the complex surface parts manufacturing process can be automated by designing a knowledge fusion model based on rough set algorithm. Such a model can be constructed by combining qualitative and quantitative knowledge resources modeling, and quality data mining. In this procedure, knowledge fusion model is used to merge knowledge resources in production process into data mining models, which could mine more abundant decision rules for various production conditions. This work exposes the correlation of knowledge resources that fuses in such indiscernibility correlation relation and the function relations. Quality prediction is shown for an operational evaluation processes and a set of prediction rules with varying precision and different structural forms. Results on quality prediction of multi axis milling for large hydraulic turbine blades show that knowledge fusion model alleviates the uncertainty of sample data and improves product quality conversely. Moreover, the prediction method is indeed beneficial to high quality and precision manufacture.

Impact of class noise on performance of hyperspectral band selection based on neighborhood rough set theory

Hyperspectral imaging has the ability for characteristic identification because of its rich physical and chemical information. To develop online or portable devices for industrial applications, informative spectral bands relevant to the specific objects are needed to be selected. The neighborhood rough set (NRS) theory is an effective tool for selecting bands. The hyperspectral datasets gathered from practical applications often contain noise. The performance of band selection algorithm is adversely influenced by noise. In this paper, the performance of the band selection algorithm in the presence of class noise was assessed, which based on consistency measure, dependency measure and information measure of the NRS theory. The robustness and classification performance of algorithms for soybean and maize classification using hyperspectral imaging were compared systematically under different levels of noise. The results demonstrate that the robustness and classifying ability of all algorithms decreases in general when the number of mislabeled samples increases in training dataset and testing datasets. Compared with other algorithms based on the NRS theory and some classic algorithms (uninformative variable elimination and successive projections algorithm), the variable precision neighborhood rough set algorithm is proven to be superior. It is more robust and more accurate when class noise occurs in training dataset or testing datasets. This research not only highlights the strong and weak characteristics of the different approaches for different levels of noise, but also provides effective solutions in handling class noise.

Local multigranulation decision-theoretic rough set in ordered information systems

As a generalized extension of Pawlak’s rough set model, the multigranulation decision-theoretic rough set model in ordered information systems utilizes the basic set assignment function to construct probability measure spaces through dominance relations. It is an effective tool to deal with uncertain problems and widely used in practical decision problems. However, when the scale of dataset is large, it takes a lot of time to characterize the approximations of the target concept, as well as complicated calculation processes. In this paper, we develop a novel model called local multigranulation decision-theoretic rough set in an ordered information system to overcome the above-mentioned limitation. Firstly, to reduce the computing time of the information granule independent of the target concept, we only use the characterization of the elements in the target concept to approximate this target concept. Moreover, the corresponding local multigranulation decision-theoretic rough set in an ordered information system is addressed according to the established local model, and the comparisons are made between the proposed local algorithm and the algorithm of original multigranulation decision-theoretic rough set in ordered information systems. Finally, the validity of the local approximation operators is verified through the experimental evaluation using six datasets coming from the University of California-Irvine (UCI) repository.

Attribute reduction algorithm of variable neighbourhood rough set model based on FCM

Based on the analysis of existing neighbourhood rough sets algorithm, a new attribute reduction algorithm, called Canopy-FCMVNRSMAR algorithm by reducing attribute using Canopy-FCM variable neighbourhood rough set model is proposed in this paper. The proposed algorithm is constructed by using attribute importance degree as the heuristic condition and makes the setting of neighbourhood value completely according to the distribution of data. So it avoids the disadvantages of setting the global neighbourhood value. The experimental results on open datasets on UCI show that the proposed algorithm can preserve fewer conditional attributes and improve the classification accuracy of data. What's more, it can extend the use of neighbourhood rough sets.

Attribute reduction algorithm of variable neighbourhood rough set model based on FCM

Based on the analysis of existing neighbourhood rough sets algorithm, a new attribute reduction algorithm, called Canopy-FCMVNRSMAR algorithm by reducing attribute using Canopy-FCM variable neighbourhood rough set model is proposed in this paper. The proposed algorithm is constructed by using attribute importance degree as the heuristic condition and makes the setting of neighbourhood value completely according to the distribution of data. So it avoids the disadvantages of setting the global neighbourhood value. The experimental results on open datasets on UCI show that the proposed algorithm can preserve fewer conditional attributes and improve the classification accuracy of data. What's more, it can extend the use of neighbourhood rough sets.

Prostate cancer classification from prostate biomedical data using ant rough set algorithm with radial trained extreme learning neural network

Prostate cancer is commonly occurs in prostate that affects small walnut and generates the seminal fluid for men. This disease is happening due to urinating trouble, blood semen, bone pain, stream of urine other harmful activities such as race, obesity and genetic changes. The improper symptoms of prostate cancer disease, it is challenge to identify it in the starting stage. So, different soft computing and machine learning techniques utilized to predict the Prostate cancer due to its severe side effects. Initially prostate cancer biomedical information has been collected from DBCR dataset that manage the patient age, cancer volume, prostate weight, Gleason score, vesicle invasion, prostate specific antigen details and so on. In the wake of gathering prostate biomedical data, undesirable information has been evacuated by applying the mean mode based standardization procedures and the advanced elements are chosen with the assistance of the subterranean insect harsh set hypothesis. The chose information has been arranged utilizing the outspread prepared extraordinary learning neural systems. The classifier successfully classifies the abnormal prostate features. At that point the effectiveness of prostate cancer prediction framework is inspected using assistance of mean square error rate, hit rate, selectivity and accuracy.

Designing Data Mining Applications with Rough Set Algorithm for Provision of Recommendations in the Selection of Training Topics on Online Learning

Online Learning is a learning model that utilizes internet network. As one of the institutions providing vocational education, ATI Immanuel is planned to implement online learning for improving the quality of education. help learners in choosing learning topics. The concept of data mining itself is a process of analysis of the database of students learning ATI Immanuel which has been collected and will be obtained patterns of student behavior in learning. The results of the study are expected to help online learning participants in this case ATI Immanuel students so they can choose the best training topics. Data mining applications with this rough set algorithm will be designed using PHP Programming Language and using System Development Life Cycle (SDLC) design method.

RoughDroid: Operative Scheme for Functional Android Malware Detection

There are thousands of malicious applications that invade Google Play Store every day and seem to be legal applications. These malicious applications have the ability to link the malware referred to as Dresscode created for network hacking as well as scrolling information. Since Android smartphones are indispensable, there should be an efficient and also unusual protection. Therefore, Android smartphones usually continue to be safeguarded from novel malware. In this paper, we propose RoughDroid, a floppy analysis technique that can discover Android malware applications directly on the smartphone. RoughDroid is based on seven feature sets (FS1,FS2,…,FS7) from the XML manifest file of an Android application, plus three feature sets (FS8,FS9, and FS10) from the Dex file. Those feature sets pass through the Rough Set algorithm to elastically classify the Android application as either benign or malicious. The experimental results mainly consider 20 most common malware families, plus three new malware families (Grabos, TrojanDropper.Agent.BKY, and AsiaHitGroup) that invade Google Play Store at 2017. According to the experimental results, RoughDroid has 95.6% detection performance for the malware families at 1% false-positive rate. Finally, RoughDroid is a lightweight approach for straightly examining downloaded applications on the smartphone.

Deep Mining of Redundant Data in Wireless Sensor Network Based on Genetic Algorithm

Mining effective data from wireless sensor network node data is one of the main subjects in studies concerning wireless sensor network data processing. Wireless sensor network data are muli-dimensional and dynamic. Generally, data mining technology cannot satisfy the requirements of wireless sensor network. A large amount of accumulated and redundant wireless sensor network monitoring data reduces the efficiency of data processing. To solve the above problems, this study proposed a data mining algorithm, which integrated rough set algorithm and genetic algorithm to mine redundant data in node network data. The results of the simulated calculation based on MATLAB platform suggested that the identification rate, false accept rate and reject rate of the proposed algorithm were 94.65, 1.753 and 2.331%; compared to network data mining algorithm based on improved genetic algorithm, it has higher efficiency and accuracy in data mining. The algorithm could effectively excavate redundant data in wireless sensor network and optimize the operation environment of wireless sensor network. The application of the rough set and genetic algorithm based data mining algorithm in wireless network has a promising prospect.

A feature selection technique based on rough set and improvised PSO algorithm (PSORS-FS) for permission based detection of Android malwares

The set of permissions required by any Android app during installation time is considered as the feature set which are used in permission based detection of Android malwares. Those high dimensional feature set should be reduced to minimize computational overhead by choosing an optimal sub set of features. In recent times, selection of meaningful attributes is an inevitable step for mining of large dimensional data and the application of heuristic feature selection algorithms are the main research directions in this field. “Quality of classification” measure is inspired by rough set theory and can be combined with bio inspired heuristic search techniques (Particle swarm optimization, Genetic Algorithm etc.) in selecting optimal or near optimal subsets of features. In this work, a feature selection technique based on rough set and improvised particle swarm optimization (PSO) algorithm is proposed for selection of features in the permission based detection of Android malwares. The main contribution of this work is to recommend a new random key encoding method which is used in the proposed work (PSORS-FS) to convert classical PSO algorithm in discrete domain. It also reduces the issues related to maximum velocity of particles as well as sigmoid function which is related with binary PSO. PSORS-FS ensures diversity in the search process and it also reduces the tendency of premature convergence. Datasets of UCI, KEEL machine learning repository and two Android permission datasets have been used to evaluate the performance of the proposed method. Better classification performance has been yielded by proposed method over conventional filters and wrapper methods for most of the machine learning classifiers.

ANALISA DAN PERANCANGAN PREDIKSI TINGKAT PRESENTASI MAHASISWA BARU MASUK SEBAGAI MAHASISWA AKTIF DI STIKOM UYELINDO KUPANG MENGGUNAKAN ROUGHT SET

Acceptance of new students is the most important part of STIKOM UYELINDO Kupang as one of the benchmarks for the progress of the campus in the future. In the process of admitting new students (PMB), prospective new students must go through several stages of registration until the stage of filling out the KRS, so that the students concerned are legitimately declared as active students of STIKOM UYELINDO KUPANG. However, many cases occur that not all students arrive at the final stage of filling in the KRS to be declared as active students. Problems that occur result in the division responsible for new students difficult to predict that prospective students concerned in the process of admitting new students, will go through the process until the status of filling KRS or not, and also affect the prediction of the number of new student achievement. This study aims to find out and recognize the pattern of classification of new student registration status so that the level of presentation of new students entering the STIKOM UYELINDO KUPANG can be made by applying the rough set algorithm. In the process of applying Rough Set, it will produce a rule as a rule or pattern for classification of new student registration status data. The data used in this study is the data of new student registration in 2016-2018 with a total record of 579 records. The results of this study are expected to be an important input for the responsibility of new students and high school education institutions, in the strategy of screening new students to achieve the target of better new student admissions.

Granular methods in automatic music genre classification: a case study

Classification of music files by using the characteristics of the songs based on its genre is a very popular application of machine learning. The focus of this work is on automatic music genre classification based on granular computing methods (fuzzy rough, rough and near sets). We have proposed a modified form of supervised learning algorithm based on tolerance near sets (TCL 2.0) with a goal of exploring the scalability of the learning algorithm to a well researched music database composed of several genres. In the tolerance near set method, tolerance classes are directly induced from the dataset using the tolerance level e and a distance function. We have compared the tolerance-based near set algorithm to a family of nearest neighbour (NN) algorithms based on fuzzy rough methods (FRNN) available in the WEKA platform. In terms of performance, the classification accuracy of TCL 2.0 is identical to the Bayesian Networks (BN) Algorithm, and comparable with the Sequential Minimal Optimization (SMO) Algorithm. However, the average classification accuracy of FRNN algorithms and the classical rough sets algorithm is better than TCL 2.0, BN and SMO algorithms. For this dataset, any accuracy over 90% is considered a very good classification accuracy which is achieved by all tested classifiers in this work.

Local neighborhood rough set

With the advent of the age of big data, a typical big data set called limited labeled big data appears. It includes a small amount of labeled data and a large amount of unlabeled data. Some existing neighborhood-based rough set algorithms work well in analyzing the rough data with numerical features. But, they face three challenges: limited labeled property of big data, computational inefficiency and over-fitting in attribute reduction when dealing with limited labeled data. In order to address the three issues, a combination of neighborhood rough set and local rough set called local neighborhood rough set (LNRS) is proposed in this paper. The corresponding concept approximation and attribute reduction algorithms designed with linear time complexity can efficiently and effectively deal with limited labeled big data. The experimental results show that the proposed local neighborhood rough set and corresponding algorithms significantly outperform its original counterpart in classical neighborhood rough set. These results will enrich the local rough set theory and enlarge its application scopes.