A Novel Subset Selection Clustering-Based Algorithm for High Dimensional Data

Balineni Bala Krishna, Kolavasi Chandra Mouli

Abstract


Feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature selection techniques are to be distinguished from feature extraction. Feature extraction creates new features from functions of the original features, whereas feature selection returns a subset of the features. Feature selection techniques are often used in domains where there are many features and comparatively few samples (or data points). It involves identifying a subset of the most useful features that produces compatible results as the original entire set of features. A feature selection algorithm may be evaluated from both the efficiency and effectiveness points of view. While the efficiency concerns the time required to find a subset of features, the effectiveness is related to the quality of the subset of features. Based on these criteria, a fast clustering-based feature selection algorithm, FAST, is proposed and experimentally evaluated in this paper. The FAST algorithm works in two steps. In the first step, features are divided into clusters by using graph-theoretic clustering methods. In the second step, the most representative feature that is strongly related to target classes is selected from each cluster to form a subset of features. Features in different clusters are relatively independent; the clustering-based strategy of FAST has a high probability of producing a subset of useful and independent features. To ensure the efficiency of FAST, we adopt the efficient minimum-spanning tree clustering method. The efficiency and effectiveness of the FAST algorithm are evaluated through an empirical study. Extensive experiments are carried out to compare FAST and several representative feature selection algorithms, namely, FCBF, ReliefF, CFS, Consist, and FOCUS-SF, with respect to four types of well-known classifiers, namely, the probability-based Naive Bayes, the tree-based C4.5, the instance-based IB1, and the rule-based RIPPER before and after feature selection. The results, on 35 publicly available real-world high dimensional image, microarray, and text data, demonstrate that FAST not only produces smaller subsets of features but also improves the performances of the four types of classifiers

References


Almuallim H. and Dietterich T.G., Algorithms for Identifying Relevant Features, In Proceedings of the 9th Canadian Conference on AI, pp 38-45, 1992.

Almuallim H. and Dietterich T.G., Learning boolean concepts in the presence of many irrelevant features, Artificial Intelligence, 69(1-2), pp 279- 305, 1994.

Arauzo-Azofra A., Benitez J.M. and Castro J.L., A feature set measure based on relief, In Proceedings of the fifth international conference on Recent Advances in Soft Computing, pp 104-109, 2004.

Baker L.D. and McCallum A.K., Distributional clustering of words for text classification, In Proceedings of the 21st Annual international ACM SIGIR Conference on Research and Development in information Retrieval, pp 96- 103, 1998.

Fayyad U. and Irani K., Multi-interval discretization of continuous-valued attributes for classification learning, In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pp 1022-1027, 1993

Dougherty, E. R., Small sample issues for microarray-based classification. Comparative and Functional Genomics, 2(1), pp 28-34, 2001.

Biesiada J. and Duch W., Features election for high-dimensionaldatała Pearson redundancy based filter, AdvancesinSoftComputing, 45, pp 242C249, 2008.

Butterworth R., Piatetsky-Shapiro G. and Simovici D.A., On Feature Selection through Clustering, In Proceedings of the Fifth IEEE international Conference on Data Mining, pp 581-584, 2005.

Cardie, C., Using decision trees to improve case-based learning, In Proceedings of Tenth International Conference on Machine Learning, pp 25-32, 1993.

Chanda P., Cho Y., Zhang A. and Ramanathan M., Mining of Attribute Interactions Using Information Theoretic Metrics, In Proceedings of IEEE international Conference on Data Mining Workshops, pp 350-355, 2009.

Chikhi S. and Benhammada S., ReliefMSS: a variation on a feature ranking ReliefF algorithm. Int. J. Bus. Intell. Data Min. 4(3/4), pp 375-390, 2009.

Cohen W., Fast Effective Rule Induction, In Proc. 12th international Conf. Machine Learning (ICML’95), pp 115-123, 1995.

Dash M. and Liu H., Feature Selection for Classification, Intelligent Data Analysis, 1(3), pp 131-156, 1997.

Dash M., Liu H. and Motoda H., Consistency based feature Selection, In Proceedings of the Fourth Pacific Asia Conference on Knowledge Discovery and Data Mining, pp 98-109, 2000.

Das S., Filters, wrappers and a boosting-based hybrid for feature Selection, In Proceedings of the Eighteenth International Conference on Machine Learning, pp 74-81, 2001.

Dash M. and Liu H., Consistency-based search in feature selection. Artificial Intelligence, 151(1-2), pp 155-176, 2003.


Full Text: PDF [Full Text]

Refbacks

  • There are currently no refbacks.


Copyright © 2013, All rights reserved.| ijseat.com

Creative Commons License
International Journal of Science Engineering and Advance Technology is licensed under a Creative Commons Attribution 3.0 Unported License.Based on a work at IJSEat , Permissions beyond the scope of this license may be available at http://creativecommons.org/licenses/by/3.0/deed.en_GB.

Â