Abstract: The feature-weighted support vector machine (FWSVM) does not take into account the correlation among the features,thus the redundancy and the interference caused by it will have a negative impact on the final classification result.A feature weighting algorithm based on inner-feature correlation and mutual information was proposed and applied in support vector machines.The algorithm introduces the inter-feature coefficient as an index to measure the redundancy,and then calculates the penalty factor to deal with the weight on the basis of the feature weighting vector machine.Thus it realizes the contribution of the feature to the classification as much as possible.The comparison of experiments on multiple data sets with several different algorithms shows that the proposed new algorithm has better robustness and generalization ability.

Key words: Correlation coefficient, Feature weighting, Mutual information, Penalty factor, Support vector machine