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A parameterless feature ranking algorithm based on MI
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DOI：10.1016/j.neucom.2007.04.012.png)
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Abstract
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A parameterless feature ranking approach is presented for feature selection in the pattern classification task. Compared with Battiti's mutual information feature selection (MIFS) and Kwak and Choi's MIFS-U methods, the proposed method derives an estimation of the conditional M I between the candidate feature f(i) and the output class C given the subset of selected features S, i.e. I(C;fi vertical bar S), without any parameters like beta in M IFS and M IFS-U methods to be preset. Thus, the intractable problem can be avoided completely, which is how to choose an appropriate value for beta to achieve the tradeoff between the relevance to the output classes and the redundancy with the already-selected features. Furthermore, a modified greedy feature selection algorithm called the second order MI feature selection approach (SOMIFS) is proposed. Experimental results demonstrate the superiority of SOMIFS in terms of both synthetic and benchmark data sets. (c) 2007 Elsevier B.V. All rights reserved.
Keywords:
FEATURE-SELECTION SCHEME
FLOATING SEARCH METHODS
INPUT FEATURE-SELECTION
MUTUAL INFORMATION
NEURAL-NETWORK
CLASSIFICATION
BRANCH
ERROR
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