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On Guo and Nixon's Criterion for Feature Subset Selection: Assumptions, Implications, and Alternative Options

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4 Author(s)
Kiran S. Balagani ; Louisiana Tech University, Ruston, LA, USA ; Vir V. Phoha ; S. S. Iyengar ; N. Balakrishnan

Guo and Nixon proposed a feature selection method based on maximizing I( x;Y), the multidimensional mutual information between feature vector x and class variable Y. Because computing I(x;Y) can be difficult in practice, Guo and Nixon proposed an approximation of I(x;Y) as the criterion for feature selection. We show that Guo and Nixon's criterion originates from approximating the joint probability distributions in I(x;Y) by second-order product distributions. We remark on the limitations of the approximation and discuss computationally attractive alternatives to compute I(x;Y) .

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IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans  (Volume:40 ,  Issue: 3 )