Conditional Mutual Information-Based Feature Selection Analyzing for Synergy and Redundancy |
Cheng, Hongrong
(Department of Computer Science, University of Electronic Science and Technology)
Qin, Zhiguang (Department of Computer Science, University of Electronic Science and Technology) Feng, Chaosheng (Department of Computer Science, University of Electronic Science and Technology) Wang, Yong (Department of Computer Science, University of Electronic Science and Technology) Li, Fagen (Department of Computer Science, University of Electronic Science and Technology) |
1 | N. Kwak and C.H. Choi, "Input Feature Selection for Classification Problems," IEEE Trans. Neural Netw., vol. 13, no. 1, 2002, pp. 143-159. DOI ScienceOn |
2 | J.J. HUANG et al., "Feature Selection for Classificatory Analysis Based on Information-Theoretic Criteria," Acta Automatica Sinica, vol. 34, no. 3, 2008, pp. 383-392. DOI |
3 | H. Peng, F. Long, and C. Ding, "Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy," IEEE Trans. Pattern Anal. Machine Intell., vol. 27, no. 8, 2005, pp. 1226-1238. DOI |
4 | P.A. Estevez et al., "Normalized Mutual Information Feature Selection," IEEE Trans. Neural Netw., vol. 20, no. 2, 2009, pp. 189-201. DOI |
5 | J. Novovicova, "Conditional Mutual Information Based Feature Selection for Classification Task," Progress Pattern Recog., Image Anal. Appl., LNCS, Springer, vol. 4756, 2007, pp. 417-426. |
6 | W.J. McGill, "Multivariate Information Transmission," Psychomeetrika, vol. 19, no. 2, 1954, pp. 97-116. DOI ScienceOn |
7 | R.M. Fano, Transmission of Information: A Statistical Theory of Communications, New York, USA: Wiley Press, 1961. |
8 | C.E. Shannon and W. Weaver, The Mathematical Theory of Communication, Urbana, Israel: University of Illinois Press, 1949. |
9 | T.M. Cover and J.A. Thomas, Elements of Information Theory, New York, USA:Wiley-Interscience Press, 1991. |
10 | U.M. Fayyad and K.B. Irani, "Multi-interval Discretization of Continuous-Valued Attributes for Classification Learning," Proc. 13th Int. Joint Conf. Artificial Intell., 1993, pp. 1022-1027. |
11 | R. Kohavi and G.H. John, "Wrappers for Feature Subset Selection," Artificial Intell., vol. 97, no. 1-2, 1997, pp. 273-324. DOI ScienceOn |
12 | D. Koller and M. Sahami, "Toward Optimal Feature Selection," Proc. 13th Int. Conf. Machine Learning, 1996, pp. 284-292. |
13 | M. Dash and H. Liu, "Feature Selection for Classification," Intelligent Data Analysis, vol. 1, 1997, pp. 131-156. DOI ScienceOn |
14 | E. Amaldi and V. Kann, "On the Approximation of Minimizing Nonzero Variables or Unsatisfied Relations in Linear Systems," Theoretical Computer Sci., vol. 209, 1998, pp. 237-260. DOI ScienceOn |
15 | I.H. Witten and E. Frank, Data Mining-Practical Machine Learning Tools and Techniques with JAVA Implementations, Morgan Kaufmann Publishers, 2nd ed., 2005. |
16 | R. Battiti, "Using Mutual Information for Selecting Features in Supervised Neural Net Learning," IEEE Trans. Neural Netw., vol. 5, no. 4, 1994, pp. 537-550. DOI ScienceOn |
17 | A. Jakulin and I. Bratko, "Quantifying and Visualizing Attribute Interactions: An Approach Based on Entropy." Available: http://arxiv.org/abs/cs.AI/0308002v3, 2004. |
18 | C.J. Merz and P.M. Murphy, "UCI Repository of Machine Learning Databases [Online]." Available: http://www.ics.uci.edu/fmlearn/MLRepository.html. |
19 | H. Peng, "mRMR Sample Data Sets [Online]." Available: http://penglab.janelia.org/proj/mRMR/test colon s3.csv. |