Research Article
Cluster Pattern Matching Using ACO Based Feature Selection for Efficient Data Classification
@INPROCEEDINGS{10.1007/978-3-642-35615-5_38, author={Sreeja N.K. and A. Sankar}, title={Cluster Pattern Matching Using ACO Based Feature Selection for Efficient Data Classification}, proceedings={Third International conference on advances in communication, network and computing}, proceedings_a={CNC}, year={2012}, month={12}, keywords={Classification Pattern matching Feature selection Ant Colony Optimization Clustering}, doi={10.1007/978-3-642-35615-5_38} }- Sreeja N.K.
A. Sankar
Year: 2012
Cluster Pattern Matching Using ACO Based Feature Selection for Efficient Data Classification
CNC
Springer
DOI: 10.1007/978-3-642-35615-5_38
Abstract
Cluster Pattern Matching based Classification (CPMC) is a classification technique based on a similarity measure between the training instances and the unknown sample. An Ant Colony Optimization based feature selection is proposed to select the features. According to this approach, the training data set is clustered. The cluster to which the unknown sample belongs is found and each of the selected features of the unknown sample is compared with the corresponding feature of the training instances in the cluster and the class of the unknown sample is predicted based on majority voting of class labels having highest number of matching patterns. A probabilistic approach is used to predict the class label when more than one class label has the same majority. Experimental results demonstrating the efficiency of classification accuracy of CPMC are shown to prove that the proposed approach is better when compared to existing classification techniques.
