Optimization of the Ho-Kashyap Classification Algorithm Using Appropriate Learning Samples

This article is focusing on optimization of the Ho-Kashyap classification algorithm. Choosing a proper learning sample plays a significant role in runtime and accuracy of the supervised classification algorithms, specially the Ho-Kashyap classification algorithm. This article with combining the methods of Multi Class Instance Selection and Ho-Kashyap, not has only reduced the starting time of algorithm, but has improved the accuracy of this algorithm, using proper parameters. The results of this suggested method, in terms of accuracy and time, are evaluated and simulations have proved that MCIS method can choose the data that have more effectiveness on classification, using proper measures. If Ho-Kashyap algorithm classifies using more important data, it could be to save the time in classification process and even increases the accuracy of classification.

Keywords:

component , Classification , Ho-Kashyap , MCIS