Feature Selection Algorithm Based on Conditional Dynamic Mutual Information

Wang Liping

doi:10.21307/ijssis-2017-761

Abstract

Aim at existing selection algorithm mutual information inaccurate valuation problem, a condition dynamic concept of mutual information. On this basis, the conditions proposed based on dynamic mutual information (CDMI) feature selection algorithm to overcome the traditional mutual information selection process dynamic correlation problem; conditions of dynamic mutual information throughout the selection process is dynamic valuation, those the samples can be identified after each selection features removed so that they no longer participate in conditional mutual information calculation process, accurate measurement sample. Accurate measurement sample on the degree of importance characteristics and at the same time ensure that the characteristics of information content. The experimental results verify the correctness and effectiveness of the algorithm.

References

Fayyad U, Piatetsky-Shapiro G, Smyth P. From Data Mining to Knowledge Discovery in Databases, AI Magazine, vol.17, pp. 37-24, 1996.10.1145/240455.240463
Search in Google Scholar Back to article
Langley P. Selection of relevant features in machine learning, Proc of the AAAI Fall Symposium on Relevance, Menlo Park, pp.140-144, 1994.10.21236/ADA292575
Search in Google Scholar Back to article
Song Q, Ni J, Wang G. A fast clustering-based feature subset selection algorithm for highdimensional data, Knowledge and Data Engineering, IEEE Transactions on, vol.25, no.1, pp.1-14, 2013.10.1109/TKDE.2011.181
Search in Google Scholar Back to article
Lopez M I, Luna J M, Romero C, et al. Classification via Clustering for Predicting Final Marks Based on Student Participation in Forums, 5th International Conference on Educational Data Mining, pp. 148-151, 2012.
Search in Google Scholar Back to article
Kira K, Rendell L. A practical approach to feature selection, Proc of the 9th International Conference on Machine Learning, pp. 249-256, 1992.10.1016/B978-1-55860-247-2.50037-1
Search in Google Scholar Back to article
Dash M, Liu H. Consistency-based search in feature selection, Artificial Intelligence, vol. 151, no.1-2, pp. 155-176, 2003.10.1016/S0004-3702(03)00079-1
Search in Google Scholar Back to article
Wei H-L, Billings S A. Feature Subset Selection and Ranking for Data Dimensionality Reduction, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, no1. 162-166, 2007.10.1109/TPAMI.2007.25060717108391
Search in Google Scholar Back to article
Yin L, Ge Y, Xiao K, et al. Feature selection for high-dimensional imbalanced data, Neurocomputing, vol. 105, pp. 3-11, 2013.10.1016/j.neucom.2012.04.039
Search in Google Scholar Back to article
Li B Q, Hu L L, Chen L, et al, Prediction of protein domain with mRMR feature selection and analysis, PLoS One, 2012, 7(6): http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039308.10.1371/journal.pone.0039308337612422720092
Search in Google Scholar Back to article
Peng H, Long F, Ding C. Feature Selection Based on Mutual Information: Criteria of MaxDependency, Max-Relevance, and Min-Redundancy, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, no.8, pp. 1226-1238, 2005.
Search in Google Scholar Back to article
Foithong S, Pinngern O, Attachoo B. Feature subset selection wrapper based on mutual information and rough sets, Expert Systems with Applications, vol.39, no.1, pp. 574-584, 2012.10.1016/j.eswa.2011.07.048
Search in Google Scholar Back to article
Guyon I, Weston J, Barnhill S, et al. Gene selection for cancer classification using support vector machines, Machine Learning, vol.46, no.1-3, pp.389-422, 2002.10.1023/A:1012487302797
Search in Google Scholar Back to article
Suzuki T, Sugiyama M. Sufficient dimension reduction via squared-loss mutual information estimation, Neural computation, vol.25, no.3, pp. 725-758, 2013.10.1162/NECO_a_0040723272920
Search in Google Scholar Back to article
Das S. Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection, Proc of the 18th International Conference on Machine Learning, San Francisco, CA, USA, pp. 74-81, 2001.
Search in Google Scholar Back to article
Li G-Z, Meng H-H, Lu W-C, et al. Asymmetric bagging and feature selection for activities prediction of drug molecules, BMC Bioinformatics, vol.9, no.S6, pp. 7-11, 2008.10.1186/1471-2105-9-S6-S7242344818541060
Search in Google Scholar Back to article
Huang D, Chow T W S. Effective feature selection scheme using mutual information, Neurocomputing, vol.63, pp. 325-343, 2005.10.1016/j.neucom.2004.01.194
Search in Google Scholar Back to article
Jain A K, Duin R P W, Mao J. Statistical Pattern Recognition: A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, no.1, pp. 4 – 37, 2000.10.1109/34.824819
Search in Google Scholar Back to article
N. K. Suryadevara and S. C. Mukhopadhyay, “Determining Wellness Through An Ambient Assisted Living Environment”, IEEE Intelligent Systems, May/June 2014, pp. 30-37.10.1109/MIS.2014.16
Search in Google Scholar Back to article
Foithong S, Pinngern O, Attachoo B. Feature subset selection wrapper based on mutual information and rough sets, Expert Systems with Applications, vol.39, no.1, pp. 574-584, 2012.10.1016/j.eswa.2011.07.048
Search in Google Scholar Back to article
N. K. Suryadevara, S. C. Mukhopadhyay, R.Wang, R.K. Rayudu and Y. M. Huang, Reliable Measurement of Wireless Sensor Network Data for Forecasting Wellness of Elderly at Smart Home, Proceedings of IEEE I2MTC 2013 conference, IEEE Catalog number CFP13IMT-CDR, ISBN 978-14673-4622-1, May 6-9, 2013, Minneapolis, USA, pp. 16-21.10.1109/I2MTC.2013.6555372
Search in Google Scholar Back to article
Ang K K, Chin Z Y, Zhang H, et al. Mutual information-based selection of optimal spatial–temporal patterns for single-trial EEG-based BCIs, Pattern Recognition, vol.45, no.6, pp. 2137-2144, 2012.
Search in Google Scholar Back to article
A. Gaddam, S.C. Mukhopadhyay and G. Sen Gupta, Necessity of a Bed Sensor in a Smart Digital Home to Care for Elder-people, Proceedings of the 2008 IEEE Sensors conference, Lecce, Italy, October 26-28, 2008, page 1340-1343.10.1109/ICSENS.2008.4716693
Search in Google Scholar Back to article
Novovicova J, Somol P, Haindl M, et al. Conditional Mutual Information Based Feature Selection for Classification Task, Proc of the 12th Iberoamericann Congress on Pattern Recognition, pp. 417-426, 2007.10.1007/978-3-540-76725-1_44
Search in Google Scholar Back to article
N.K. Suryadevara, S.C. Mukhopadhyay, R. Wang, R.K. Rayudu, Forecasting the behavior of an elderly using wireless sensors data in a smart home, Engineering Applications of Artificial Intelligence, Volume 26, Issue 10, November 2013, Pages 2641-2652, ISSN 0952-1976, http://dx.doi.org/10.1016/j.engappai.2013.08.004.10.1016/j.engappai.2013.08.004
Search in Google Scholar Back to article
Daode Zhang et al., Research on Chip Defect Extraction based on Image-Matching, International Journal on Smart Sensing and Intelligent Systems, vol. 7, no. 1, pp.321 – 336, 2014.10.21307/ijssis-2017-658
Search in Google Scholar Back to article
N.K.Suryadevara, A. Gaddam, R.K.Rayudu and S.C. Mukhopadhyay, “Wireless Sensors Network based safe Home to care Elderly People: Behaviour Detection”, Sens. Actuators A: Phys. (2012), doi:10.1016/j.sna.2012.03.020, Volume 186, 2012, pp. 277 – 283.10.1016/j.sna.2012.03.020
Search in Google Scholar Back to article
Yanmin LUO, Peizhong LIU and Minghong LIAO, An Artificial Immune Network Clustering Algorithm for Mangroves Remote Sensing Iamge,International Journal on Smart Sensing and Intelligent Systems, vol. 7, no. 1, pp. 116 – 134, 2014.10.21307/ijssis-2017-648
Search in Google Scholar Back to article

Feature Selection Algorithm Based on Conditional Dynamic Mutual Information

Abstract

Paradigm

My account