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A Genetic Algorithm Based Intuitionistic Fuzzification Technique for Attribute Selection

Affiliations

  • Department of Computer Science, Karpagam University, Coimbatore-21, TN, India

Abstract


This paper initiates perceptions and algorithms of feature selection, survey of existing feature selection algorithms and assesses diverse algorithms with a classifying frame based on search approaches, valuation criteria, and provides strategy in selecting feature selection algorithms. A unifying platform is projected to continue our efforts headed for building an incorporated system for intelligent feature selection. Feature selection intends to reduce the dimensionality of patterns for classification by choosing the most informative instead of irrelevant and/or redundant features. In this proposed work Intuitionistic fuzzy based feature clustering is proposed for grouping features based on the degree of membership and degree of indeterminacy among the attributes and clusters. In this proposed work a novel approach which uses an Intuitionistic fuzzy version of k-means has been introduced for grouping interdependent features. Genetic algorithm reinstating which is the variation of traditional genetic algorithm is then applied to appraise whether the measured feature is independent of class labels; hence, it leads to eliminate unrelated clusters to classification process and progress the selection of features. The proposed method achieves improvement on classification accuracy and perhaps to select less number of features which show the way to simplification of learning task to a big extent. The Experiment results have been demonstrated by the good performance and also find good enough subset features of this method on using UCI benchmark datasets that are for data mining methods such as Breast Cancer, Sensor and Iris Records.

Keywords

Feature Selection, Cluster, Genetic, K-means, Fuzzy, Intuitionistic

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References


  • Masud M M, Chen O et al. (2012). Classification and adaptive novel class detection of feature evolving data streams, IEEE Transactions on Knowledge and Data Engineering, vol PP(99).
  • Wu X, Yu K et al. (2012). Online feature selection with streaming features, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 35(99), 1178-1192.
  • Dai B, Huang J et al. (2006). Adaptive clustering for multiple evolving streams, IEEE Transactions on Knowledge and Data Engineering, vol 18(9), 1166-1180.
  • Song O, Ni J et al. (2013). A fast clustering-based feature subset selection algorithm for high-dimensional data, IEEE Transactions on Knowledge and Data Engineering, vol 25 (1), 1-14.
  • Chouaib H, Terrades O R et al. (2008). Feature selection combining genetic algorithm and Adaboost classifiers, 19th International Conference on Pattern Recognition, 2008. ICPR 2008, 1-4.
  • Wasikowski M, and Chen X (2010). Combating the small sample class imbalance problem using feature selection, IEEE Transactions on Knowledge and Data Engineering, vol 22(10), 1388-1400.
  • Guyon I, and Elisseeff A (2003). An introduction to variable and feature selection, Journal of Machine Learning Research, vol 3, 1157-1182.
  • Dash M, and Liu H (1997). Feature selection for classification, Intelligent Data Analysis, vol 1(1-4), 131-156.
  • Jain A K, and Zongker D E (1997). Feature selection: evaluation, application, and small sample performance, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 19(2), 153-158.
  • Koller D, and Sahami M (1996). Toward optimal feature selection, Proceedings 13th International Conference Machine Learning, 284-292.
  • Yu L, and Liu H (2004). Efficient feature selection via analysis of relevance and redundancy, Journal of Machine Learning Research, vol 5, 1205-1224.
  • Dash M, and Liu H (1997). Feature selection for classification, Intelligent Data Analysis, Elsevier Science B.V., vol. 1(3), 131-156.
  • Zhu Z, Ong Y et al. (2007). Wrapper filter feature selection algorithm using a memetic framework, IEEE Transactions on Systems, Man and Cybernetics, vol.37 (1), 70-76.
  • Au W H, Chan K C C et al. (2005). Attribute clustering for grouping, selection, and classification of gene expression Data, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 2(2), 83-101.
  • Wei H L, and Billings S A (2007). Feature subset selection and ranking for data dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29(1), 162-166.
  • Tan K C, Teoh E J et al. (2008). A hybrid evolutionary algorithm for attribute selection in data mining, Expert Systems with Applications, vol 36(4), 8616-8630.
  • Maji P (2012). Mutual information-based supervised attribute clustering for microarray sample classification, IEEE Transactions on Knowledge and Data Engineering, vol. 24(1), 127-140.
  • Javed K, Babri H A et al. (2012). Feature selection based on class-dependent densities for high-dimensional binary data, IEEE Transactions on Knowledge and Data Engineering, vol 24(3), 465-477.
  • Zhang S, Wong H et al. (2012). A new unsupervised feature ranking method for gene expression data based on consensus affinity, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol 9(4), 1257-1263.
  • Yang S H and Hu B G (2012). Discriminative feature selection by nonparametric bayes error minimization, IEEE Transactions on Knowledge and Data Engineering, vol 24(8), 1422-1434.
  • Esmaeili M, and Gabor F (2011). Feature selection as an improving step for decision tree construction, Proceedings of International Conference on Machine Learning and Computing, vol 3, 35-39.
  • Tsai D, and Lin C (2011). Fuzzy C-means based clustering for linearly and nonlinearly separable data, Pattern Recognition, vol 44 (8), 1750-1760.
  • Gonzalez A, and Pérez R (2001). Selection of relevant features in a fuzzy genetic learning algorithm, IEEE Transactions on Systems, Man and Cybernetics, vol 31(3), 417-425.
  • Huang C L, and Wang C (2006). A GA-based feature selection and parameters optimization for support vector machines, Expert Systems with Applications, vol 31(2), 231-240.
  • Tan F, Fu X (2008). A genetic algorithm-based method for feature subset selection, Soft Computing, vol 12 (2).111-120.
  • Hoi C H S, Wang J et al. (2012). Online feature selection for mining big data, BigMine’12, 93-100.
  • Chitsaz E, Taheri M et al. (2008). A Fuzzy approach to clustering and selecting features for classification of gene expression data, Proceedings of the World Congress on Engineering, vol II.
  • Chang C T, Lai J Z C et al. (2011). A Fuzzy K-means clustering algorithm using cluster center displacement, Journal of Information Science and Engineering, vol 27, 995-1009.
  • Atanassov K T (1986). Intuitionistic fuzzy sets, Fuzzy Sets and Systems, vol20, 87-96.
  • Al-Harbi S H, and Rayward-Smith V J (2006). Adapting k-means for supervised clustering, Applied Intelligence, vol 24 (3), 219-226.
  • Chitsa Z E, Taheri M et al. (2009). An improved fuzzy feature clustering and selection based on Chi-Squared-Test, Proceeding of the International Multi Conference of Engineers and Computer Scientists.
  • Tsang E C C, Yeung D S et al. (2003). OFFSS: Optimal fuzzy-valued feature subset selection, IEEE Transactions on Fuzzy Systems, vol11 (2), 202-213.
  • Pizzileo B, Li K et al. (2012). Improved structure optimization for fuzzy-neural networks, IEEE Transactions on Fuzzy Systems, vol 20(6), 1076-1089.
  • Arima C, Hanai T et al. (2003). Gene expression analysis using fuzzy k-means clustering, Genome Informatics, vol 14(1), 334-335.
  • Deng Z, Chung F L et al. (2010). Robust relief-feature weighting, margin maximization and fuzzy optimization, IEEE Transactions on Fuzzy Systems, vol 18(4), 726-744.
  • Yager R R (2009). Some aspects of Intuitionistic Fuzzy sets, Fuzzy Optimization and Decision Making, vol 8(1), 67-90.
  • Hu O, Pan W et al. (2012). Feature selection for monotonic classification, IEEE Transactions on Fuzzy Systems, vol 20(1), 69-81.
  • Maji P (2011). Fuzzy rough supervised attribute clustering algorithm and classification of microarray data, IEEE Transactions on Systems, Man, and Cybernetics, vol 41(1), 222-233.

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