earticle

영어

The feature selection approach has been regarded as an effective way to remove redundant and irrelevant features. Thus it increases the efficiency of the learning task and improves the learning performance such as learning time, convergence rate, accuracy, etc. Machine learning approaches such as Neural network, Decision tree, Support Vector Machines are well suited for domains characterized by the presence of large amount of data, noisy patterns, and absence of general theory. The main goal of our research is to propose an efficient feature selection algorithm to achieve a cancer diagnosis system with high accuracies, and good adaptability to clinical dataset. We propose a new feature selection algorithm using Random Forest(RF) to be applicable to the learning algorithm for diagnosing Cancer. The experiments on clinical dataset such as leukemia cancer indicate that our proposed methods obtain higher and more stable classification performance than the baseline methods.