Swarm Intelligence Algorithms for Feature Selection: A Review

The increasingly rapid creation, sharing and exchange of information nowadays put researchers and data scientists ahead of a challenging task of data analysis and extracting relevant information out of data. To be able to learn from data, the dimensionality of the data should be reduced first. Feature selection (FS) can help to reduce the amount of data, but it is a very complex and computationally demanding task, especially in the case of high-dimensional datasets. Swarm intelligence (SI) has been proved as a technique which can solve NP-hard (Non-deterministic Polynomial time) computational problems. It is gaining popularity in solving different optimization problems and has been used successfully for FS in some applications. With the lack of comprehensive surveys in this field, it was our objective to fill the gap in coverage of SI algorithms for FS. We performed a comprehensive literature review of SI algorithms and provide a detailed overview of 64 different SI algorithms for FS, organized into eight major taxonomic categories. We propose a unified SI framework and use it to explain different approaches to FS. Different methods, techniques, and their settings are explained, which have been used for various FS aspects. The datasets used most frequently for the evaluation of SI algorithms for FS are presented, as well as the most common application areas. The guidelines on how to develop SI approaches for FS are provided to support researchers and analysts in their data mining tasks and endeavors while existing issues and open questions are being discussed. In this manner, using the proposed framework and the provided explanations, one should be able to design an SI approach to be used for a specific FS problem

PARTICLE SWARM OPTIMIZATION IN FEATURE SELECTION FOR CLASSIFICATION

V magistrskem delu smo razvili metodo FS-BPSO, ki združuje postopek izbire atributov z algoritmom optimizacije z rojem delcev. Glavni namen te metode je njena uporabnost pri reševanju naslednjega dobro znanega problema. Ko so v podatkovni množici primerki z ogromnim številom atributov, je med njimi težko najti tiste, ki so najbolj informativni oziroma reprezentativni. Tega problema smo se lotili s predlaganim hibridnim algoritmom binarne optimizacije z rojem delcev v kombinaciji s klasifikacijskimi metodami C4.5, Naive Bayes in SVM v cenitveni funkciji za izbiro informativnih atributov. Dobljeni rezultati so statistično analizirani in razkrivajo, da predlagani hibridni algoritem prekaša znane klasifikacijske metode C4.5, Naive Bayes in SVM.In this master\u27s thesis, we have developed an FS-BPSO method that joins a feature selection procedure with a particle swarm optimization algorithm. The main purpose of this method is its usability in addressing the following well-known problem: When there are instances with a huge number of attributes in a data set, it is hard to select the most representative ones among them. In order to cope with this problem, we propose the use of a hybrid binary particle swarm optimization algorithm combined with C4.5, Naive Bayes, and SVM as the classifiers in the fitness function for the selection of informative attributes. The results obtained were statistically analysed and revealed that the proposed algorithm outperformed known classifiers, e.g. C4.5, Naive Bayes, and SVM

DEVELOPMENT OF A DECISION SUPPORT SYSTEM USING THE NON-DOMINATED SORTING

V diplomskem delu smo razvili spletni informacijski sistem, ki uporabniku pomaga izbrati najboljšo alternativo (rešitev) večkriterijskega odločitvenega problema. Sistem razvrsti (rangira) alternative v več skupin, od najboljše skupine do najslabše. Znotraj posamezne skupine so vse alternative enakovredne. Uporabnikova naloga je le, da glede na lastne izkušnje in dane okoliščine, izbere najbolj ugodno alternativo v najboljši skupini. Sistem je univerzalen, zato omogoča reševanje raznovrstnih odločitvenih problemov. Za razvrstitev (rangiranje) alternativ po prednostnem vrstnem redu smo uporabili modul za nedominirano razvrščanje, ki temelji na Pareto optimalnosti in je napisan v programskem jeziku lisp. Preostali moduli sistema so napisani v javi in z njim komunicirajo prek datotek oblike LSP in XML. Spletna podpora je izvedena s sodobnimi tehnologijami, med katerimi so EJB, JSF, PrimeFaces, jQuery, JavaScript, AJAX, XHTML in CSS3. Delovanje sistema z uporabnikovega zornega kota smo ilustrirali z dvema primeroma. Prvi prikazuje splošni izbor optimalnih alternativ, ko uporabnik poda število alternativ in kriterijev ter obseg vrednosti za vsak kriterij, vrednosti kriterijev za vsako alternativo pa se generirajo naključno, drugi pa izbiro optimalnega dobavitelja z ozirom na ceno, kakovost in dobavni rok izdelka ter renome in oddaljenost dobavitelja.In this diploma work, we have developed a web-based information system that helps a user to choose the best alternative (solution) regarding a multicriteria decision problem. This system sorts (ranks) alternatives into several groups, from the best to the worst group. All alternatives within each group are equivalent. According to the user’s experiences and the circumstances, his/her task is just to choose the most favourable alternative within the best group. This system is universal and thus allows the solving of a wide range of decision problems. We have used a module for non-dominated sorting based on Pareto optimality and written in Lisp programming language for sorting (ranking) alternatives in the preferred order. The remaining modules of the system are written in Java and communicate with it through the files of types LSP and XML, respectively. Web support is implemented using modern technologies including EJB, JSF, PrimeFaces, jQuery, JavaScript, AJAX, XHTML, and CSS3. Operation of the system from the user\u27s perspective is illustrated using two examples. The first one shows the general selection of optimal alternatives when the user specifies the number of alternatives, the number of criteria, and the range of values for each criterion, whilst the criteria values for each alternative are generated randomly. The second one presents the choice of optimal supplier with respect to price, quality, and delivery time of the product, as well as of the reputation and remoteness of the supplier