An Unsupervised Feature Selection Algorithm with Feature Ranking for Maximizing Performance of the Classifiers

Danasingh Asir Antony Gnana Singh; Subramanian Appavu Alias Balamurugan; Epiphany Jebamalar Leavline

doi:10.1007/s11633-014-0859-5

October 2015

Article Contents

Danasingh Asir Antony Gnana Singh, Subramanian Appavu Alias Balamurugan and Epiphany Jebamalar Leavline. An Unsupervised Feature Selection Algorithm with Feature Ranking for Maximizing Performance of the Classifiers. International Journal of Automation and Computing, vol. 12, no. 5, pp. 511-517, 2015. doi: 10.1007/s11633-014-0859-5

Cite as: Danasingh Asir Antony Gnana Singh, Subramanian Appavu Alias Balamurugan and Epiphany Jebamalar Leavline. An Unsupervised Feature Selection Algorithm with Feature Ranking for Maximizing Performance of the Classifiers. International Journal of Automation and Computing, vol. 12, no. 5, pp. 511-517, 2015. doi: 10.1007/s11633-014-0859-5

An Unsupervised Feature Selection Algorithm with Feature Ranking for Maximizing Performance of the Classifiers

Department of Computer Science and Engineering, Bharathidasan Institute of Technology, Anna University, Tiruchirappalli 620024, India;
Department of Information Technology, K. L. N College of Information Technology, Sivagangai 630611, India;
Department of Electronics and Communication Engineering, Bharathidasan Institute of Technology, Anna University, Tiruchirappalli 620024, India

Received: 2013-11-04

Abstract

Prediction plays a vital role in decision making. Correct prediction leads to right decision making to save the life, energy, efforts, money and time. The right decision prevents physical and material losses and it is practiced in all the fields including medical, finance, environmental studies, engineering and emerging technologies. Prediction is carried out by a model called classifier. The predictive accuracy of the classifier highly depends on the training datasets utilized for training the classifier. The irrelevant and redundant features of the training dataset reduce the accuracy of the classifier. Hence, the irrelevant and redundant features must be removed from the training dataset through the process known as feature selection. This paper proposes a feature selection algorithm namely unsupervised learning with ranking based feature selection (FSULR). It removes redundant features by clustering and eliminates irrelevant features by statistical measures to select the most significant features from the training dataset. The performance of this proposed algorithm is compared with the other seven feature selection algorithms by well known classifiers namely naive Bayes (NB), instance based (IB1) and tree based J48. Experimental results show that the proposed algorithm yields better prediction accuracy for classifiers.
- Feature selection algorithm,
- classification,
- clustering,
- prediction,
- feature ranking,
- predictive model

References

[1]	J. Sinno, Q. Y. Pan. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, vol.,22, no.,10, pp.,1345-135, 2010.
[2]	M. R. Rashedur, R. M. Fazle. Using and comparing different decision tree classification techniques for mining ICDDR, B Hospital Surveillance data. Expert Systems with Applications, vol.,38, no.,9, pp.,11421-11436, 2011.
[3]	M. Wasikowski, X. W. Chen. Combating the small sample class imbalance problem using feature selection. IEEE Transactions on Knowledge and Data Engineering, vol.,22, no.,10, pp.,1388-1400, 2010.
[4]	Q. B. Song, J. J. Ni, G. T. Wang. A fast clustering-based feature subset selection algorithm for high-dimensional data. IEEE Transactions on Knowledge and Data Engineering, vol.,l5, no.,1, pp.,1-14, 2013.
[5]	J. F. Artur, A. T. Mário. Efficient feature selection filters for high-dimensional data. Pattern Recognition Letters, vol.,33, no.,13, pp.,1794-1804, 2012.
[6]	J. Wu, L. Chen, Y. P. Feng, Z. B. Zheng, M. C. Zhou, Z. H. Wu. Predicting quality of service for selection by neighborhood-based collaborative filtering. IEEE Transactions on Systems, Man, and Cybernetics:Systems, vol.,43, no.,2, pp.,428-439, 2012.
[7]	C. P. Hou, F. P. Nie, X. Li, D. Yi, Y. Wu. Joint embedding learning and sparse regression:A framework for unsupervised feature selection. IEEE Transactions on Cybernetics, vol.,44, no.,6, pp.,793-804, 2014.
[8]	P. Bermejo, L. dela Ossa, J. A. Gámez, J. M. Puerta. Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking Original Research Article. Knowledge-based Systems, vol.,25, no.,1, pp.,35-44, 2012.
[9]	S. Atulji, G. Shameek, V. K. Jayaramanb. Hybrid biogeography based simultaneous feature selection and MHC class I peptide binding prediction using support vector machines and random forests. Journal of Immunological Methods, vol.,387, no.,1-2, pp.,284-292, 2013.
[10]	H. L. Wei, S. Billings. Feature subset selection and ranking for data dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.,29, no.,1, pp.,162-166, 2007.
[11]	X. V. Nguyen, B. James. Comments on supervised feature selection by clustering using conditional mutual information-based distances. Pattern Recognition, vol.,46, no.,4, pp.,1220-1225, 2013.
[12]	A. G. Iffat, S. S. Leslie. Feature subset selection in large dimensionality domains. Pattern Recognition, vol.,43, no.,1, pp.,5-13, 2010.
[13]	M. Hall. Correlation-based Feature Selection for Machine Learning, Ph.,D dissertation, The University of Waikato, New Zealond, 1999.
[14]	Y. Lei, L. Huan. Efficient feature selection via analysis of relevance and redundancy. Journal of Machine Learning Research, vol.,5, no.,1, pp.,1205-1224, 2004.
[15]	M. Dash, H. Liu, H. Motoda. Consistency based feature selection. In Proceedings of the 4th Pacific Asia Conference on Knowledge Discovery and Data Mining, Kyoto, Japan, pp.,98-109, 2000.
[16]	H. Peng, L. Fulmi, C. Ding. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.,27, no.,8, pp.,1226-1238, 2005.
[17]	H. Uğuz. A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm. Knowledge-based Systems, vol.,24, no.,7, pp.,1024-1032, 2011.
[18]	M. Robnik-Šikonja, I. Kononenko. Theoretical and empirical analysis of ReliefF and RReliefF. Machine Learning, vol.,53, no.,1-2, pp.,23-69, 2003.
[19]	S. Yijun, T. Sinisa, G. Steve. Local-learning-based feature selection for high-dimensional data analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.,32, no.,9, pp.,1610-1626, 2010.
[20]	W. Peng, S. Cesar, S. Edward. Prediction based on integration of decisional DNA and a feature selection algorithm RELIEF-F. Cybernetics and Systems, vol.,44, no.,3, pp.,173-183, 2013.
[21]	H. W. Liu, J. G. Sun, L. Liu, H. J. Zhang. Feature selection with dynamic mutual information. Pattern Recognition, vol.,42, no.,7, pp.,1330-1339, 2009.
[22]	M. C. Lee. Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Systems with Applications, vol.,36, no.,8, pp.,10896-10904, 2009.
[23]	P. Mitra, C. A. Murthy, S. K. Pal. Unsupervised feature selection using feature similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.,24, no.,3, pp.,301-312, 2002.
[24]	J. Handl, J. Knowles. Feature subset selection in unsupervised learning via multi objective optimization. International Journal of Computational Intelligence Research, vol.,2, no.,3, pp.,217-238, 2006.
[25]	H. Liu, L. Yu. Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, vol.,17, no.,4, pp.,491-502, 2005.
[26]	S. García, J. Luengo, J. A. Sáez, V. Lóez, F. Herrera. A survey of discretization techniques:Taxonomy and empirical analysis in supervised learning. IEEE Transactions on Knowledge and Data Engineering, vol.,25, no.,4, pp.,734-750, 2013.
[27]	S. A. A. Balamurugan, R. Rajaram. Effective and efficient feature selection for large-scale data using Bayes' theorem. International Journal of Automation and Computing, vol.,6, no.,1, pp.,62-71, 2009.
[28]	J. A. Mangai, V. S. Kumar, S. A. alias Balamurugan. A novel feature selection framework for automatic web page classification. International Journal of Automation and Computing, vol.,9, no.,4, pp.,442-448, 2012.
[29]	H. J. Huang, C. N. Hsu. Bayesian classification for data from the same unknown class. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetic, vol.,32, no.,2, pp.,137-145, 2002.
[30]	S. Ruggieri. Efficient C4.5. IEEE Transactions on Knowledge and Data Engineering, vol.,14, no.,2, pp.,438-444, 2002.
[31]	P. Kemal, G. Salih. A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems. Expert Systems with Applications, vol.,36, no.,2, pp.,1587-1592, 2009.
[32]	W. W. Cheng, E. Höullermeier. Combining instance-based learning and logistic regression for multilabel classification. Machine Learning, vol.,76, no.,3, pp.,211-225, 2009.
[33]	J. H. Hsiu, P. Saumyadipta, I. L. Tsung. Maximum likelihood inference for mixtures of skew Student-t-normal distributions through practical EM-type algorithms. Statistics and Computing, vol.,22, no.,1, pp.,287-299, 2012.
[34]	M. H. C. Law, M. A. T. Jain, A. K. Jain. Simultaneous feature selection and clustering using mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.,26, no.,9, pp.,1154-1166, 2004.
[35]	T. W. Lee, M. S. Lewicki, T. J. Sejnowski. ICA mixture models for unsupervised classification of non-gaussian classes and automatic context switching in blind signal separation. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.,22, no.,10, pp.,1078-1089, 2002.
[36]	M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann. The WEKA data mining software:An update. ACM SIGKDD Explorations Newsletter, vol.,11, no.,1, pp.,10-18, 2009.

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An Unsupervised Feature Selection Algorithm with Feature Ranking for Maximizing Performance of the Classifiers

Department of Computer Science and Engineering, Bharathidasan Institute of Technology, Anna University, Tiruchirappalli 620024, India;

Department of Information Technology, K. L. N College of Information Technology, Sivagangai 630611, India;

Department of Electronics and Communication Engineering, Bharathidasan Institute of Technology, Anna University, Tiruchirappalli 620024, India

Received: 2013-11-04

Key words:

Abstract: Prediction plays a vital role in decision making. Correct prediction leads to right decision making to save the life, energy, efforts, money and time. The right decision prevents physical and material losses and it is practiced in all the fields including medical, finance, environmental studies, engineering and emerging technologies. Prediction is carried out by a model called classifier. The predictive accuracy of the classifier highly depends on the training datasets utilized for training the classifier. The irrelevant and redundant features of the training dataset reduce the accuracy of the classifier. Hence, the irrelevant and redundant features must be removed from the training dataset through the process known as feature selection. This paper proposes a feature selection algorithm namely unsupervised learning with ranking based feature selection (FSULR). It removes redundant features by clustering and eliminates irrelevant features by statistical measures to select the most significant features from the training dataset. The performance of this proposed algorithm is compared with the other seven feature selection algorithms by well known classifiers namely naive Bayes (NB), instance based (IB1) and tree based J48. Experimental results show that the proposed algorithm yields better prediction accuracy for classifiers.

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Reference (36)

[1]	J. Sinno, Q. Y. Pan. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, vol.,22, no.,10, pp.,1345-135, 2010.
[2]	M. R. Rashedur, R. M. Fazle. Using and comparing different decision tree classification techniques for mining ICDDR, B Hospital Surveillance data. Expert Systems with Applications, vol.,38, no.,9, pp.,11421-11436, 2011.
[3]	M. Wasikowski, X. W. Chen. Combating the small sample class imbalance problem using feature selection. IEEE Transactions on Knowledge and Data Engineering, vol.,22, no.,10, pp.,1388-1400, 2010.
[4]	Q. B. Song, J. J. Ni, G. T. Wang. A fast clustering-based feature subset selection algorithm for high-dimensional data. IEEE Transactions on Knowledge and Data Engineering, vol.,l5, no.,1, pp.,1-14, 2013.
[5]	J. F. Artur, A. T. Mário. Efficient feature selection filters for high-dimensional data. Pattern Recognition Letters, vol.,33, no.,13, pp.,1794-1804, 2012.
[6]	J. Wu, L. Chen, Y. P. Feng, Z. B. Zheng, M. C. Zhou, Z. H. Wu. Predicting quality of service for selection by neighborhood-based collaborative filtering. IEEE Transactions on Systems, Man, and Cybernetics:Systems, vol.,43, no.,2, pp.,428-439, 2012.
[7]	C. P. Hou, F. P. Nie, X. Li, D. Yi, Y. Wu. Joint embedding learning and sparse regression:A framework for unsupervised feature selection. IEEE Transactions on Cybernetics, vol.,44, no.,6, pp.,793-804, 2014.
[8]	P. Bermejo, L. dela Ossa, J. A. Gámez, J. M. Puerta. Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking Original Research Article. Knowledge-based Systems, vol.,25, no.,1, pp.,35-44, 2012.
[9]	S. Atulji, G. Shameek, V. K. Jayaramanb. Hybrid biogeography based simultaneous feature selection and MHC class I peptide binding prediction using support vector machines and random forests. Journal of Immunological Methods, vol.,387, no.,1-2, pp.,284-292, 2013.
[10]	H. L. Wei, S. Billings. Feature subset selection and ranking for data dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.,29, no.,1, pp.,162-166, 2007.
[11]	X. V. Nguyen, B. James. Comments on supervised feature selection by clustering using conditional mutual information-based distances. Pattern Recognition, vol.,46, no.,4, pp.,1220-1225, 2013.
[12]	A. G. Iffat, S. S. Leslie. Feature subset selection in large dimensionality domains. Pattern Recognition, vol.,43, no.,1, pp.,5-13, 2010.
[13]	M. Hall. Correlation-based Feature Selection for Machine Learning, Ph.,D dissertation, The University of Waikato, New Zealond, 1999.
[14]	Y. Lei, L. Huan. Efficient feature selection via analysis of relevance and redundancy. Journal of Machine Learning Research, vol.,5, no.,1, pp.,1205-1224, 2004.
[15]	M. Dash, H. Liu, H. Motoda. Consistency based feature selection. In Proceedings of the 4th Pacific Asia Conference on Knowledge Discovery and Data Mining, Kyoto, Japan, pp.,98-109, 2000.
[16]	H. Peng, L. Fulmi, C. Ding. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.,27, no.,8, pp.,1226-1238, 2005.
[17]	H. Uğuz. A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm. Knowledge-based Systems, vol.,24, no.,7, pp.,1024-1032, 2011.
[18]	M. Robnik-Šikonja, I. Kononenko. Theoretical and empirical analysis of ReliefF and RReliefF. Machine Learning, vol.,53, no.,1-2, pp.,23-69, 2003.
[19]	S. Yijun, T. Sinisa, G. Steve. Local-learning-based feature selection for high-dimensional data analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.,32, no.,9, pp.,1610-1626, 2010.
[20]	W. Peng, S. Cesar, S. Edward. Prediction based on integration of decisional DNA and a feature selection algorithm RELIEF-F. Cybernetics and Systems, vol.,44, no.,3, pp.,173-183, 2013.
[21]	H. W. Liu, J. G. Sun, L. Liu, H. J. Zhang. Feature selection with dynamic mutual information. Pattern Recognition, vol.,42, no.,7, pp.,1330-1339, 2009.
[22]	M. C. Lee. Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Systems with Applications, vol.,36, no.,8, pp.,10896-10904, 2009.
[23]	P. Mitra, C. A. Murthy, S. K. Pal. Unsupervised feature selection using feature similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.,24, no.,3, pp.,301-312, 2002.
[24]	J. Handl, J. Knowles. Feature subset selection in unsupervised learning via multi objective optimization. International Journal of Computational Intelligence Research, vol.,2, no.,3, pp.,217-238, 2006.
[25]	H. Liu, L. Yu. Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, vol.,17, no.,4, pp.,491-502, 2005.
[26]	S. García, J. Luengo, J. A. Sáez, V. Lóez, F. Herrera. A survey of discretization techniques:Taxonomy and empirical analysis in supervised learning. IEEE Transactions on Knowledge and Data Engineering, vol.,25, no.,4, pp.,734-750, 2013.
[27]	S. A. A. Balamurugan, R. Rajaram. Effective and efficient feature selection for large-scale data using Bayes' theorem. International Journal of Automation and Computing, vol.,6, no.,1, pp.,62-71, 2009.
[28]	J. A. Mangai, V. S. Kumar, S. A. alias Balamurugan. A novel feature selection framework for automatic web page classification. International Journal of Automation and Computing, vol.,9, no.,4, pp.,442-448, 2012.
[29]	H. J. Huang, C. N. Hsu. Bayesian classification for data from the same unknown class. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetic, vol.,32, no.,2, pp.,137-145, 2002.
[30]	S. Ruggieri. Efficient C4.5. IEEE Transactions on Knowledge and Data Engineering, vol.,14, no.,2, pp.,438-444, 2002.
[31]	P. Kemal, G. Salih. A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems. Expert Systems with Applications, vol.,36, no.,2, pp.,1587-1592, 2009.
[32]	W. W. Cheng, E. Höullermeier. Combining instance-based learning and logistic regression for multilabel classification. Machine Learning, vol.,76, no.,3, pp.,211-225, 2009.
[33]	J. H. Hsiu, P. Saumyadipta, I. L. Tsung. Maximum likelihood inference for mixtures of skew Student-t-normal distributions through practical EM-type algorithms. Statistics and Computing, vol.,22, no.,1, pp.,287-299, 2012.
[34]	M. H. C. Law, M. A. T. Jain, A. K. Jain. Simultaneous feature selection and clustering using mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.,26, no.,9, pp.,1154-1166, 2004.
[35]	T. W. Lee, M. S. Lewicki, T. J. Sejnowski. ICA mixture models for unsupervised classification of non-gaussian classes and automatic context switching in blind signal separation. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.,22, no.,10, pp.,1078-1089, 2002.
[36]	M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann. The WEKA data mining software:An update. ACM SIGKDD Explorations Newsletter, vol.,11, no.,1, pp.,10-18, 2009.

An Unsupervised Feature Selection Algorithm with Feature Ranking for Maximizing Performance of the Classifiers

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