A new SVM decision tree multi-class classification algorithm based on Mahalanobis distance
Zhihua Diao, Yuanyuan Wu · Chinese Control Conference · 2011
In order to avoid the disadvantages of treating the differences between different attributes of the samples equally and taking no account of the correlativity of different variables in computing the inter-class separability measure in European space, we proposed a method of computing the inter-class separability measure based on Mahalanobis distance, and gained a multi-class classifying algorithm based on SVM and decision tree utilizing the advantages that the Mahalanobis distance has dimensionless impact and has nothing to do with the unit of measurements with the original data. Experimental results show that the classifying project we obtained by this algorithm is a better one and this algorithm could have a higher recognition rate, and the algorithm is an effective multi-class classifying algorithm.