Using misclassified training samples to improve classification
This paper proposes an improved classification strategy using misclassified training samples. It is shown that a subset of the misclassified training set forms isolated pockets. In the proposed approach, apart from providing the parameters derived out of the training samples to a classifier, the location of these misclassified pockets is also provided. The proposed strategy overcomes any weakness a given classifier may have by changing the classification decision for a given test sample based on the location of the test sample with respect to the misclassified pockets. Three diversely different classifiers and a simple composite classifier are used to test the strategy. The proposed strategy is implemented on both simulated and real data and it is shown that a reduced error rate can be obtained when this strategy is used.
|Conference||Proceedings of the 1998 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5)|
Balasubramanian, Ram (Ram), Rajan, S, Doraiswami, Rajamani (Rajamani), & Stevenson, Maryhelen (Maryhelen). (1998). Using misclassified training samples to improve classification. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (pp. 4296–4300).