抽象的

Evaluating the Effectiveness of Classification Algorithms Based on CCI

R. Srujana , Dr. G S N Murty

Machine Learning has been widely applied to various domains and has gained a lot of success. At present, various learning algorithms are available, still facing difficulties in choosing the best methods that can be applied to their data. In this paper we perform an empirical study on 9 individual learning algorithms on a dataset by analyzing their performances and provide some Rules-of-thumb on selecting the algorithm over the dataset. To evaluate the performance, here we suggested supervised learning algorithm which can compute faster and better over the defined set of algorithms based on Time Complexity and Confusion Matrix. To assess the results over the given dataset, Receiver Operating Characteristic (ROC) curve is plotted on a graph by sensitivity or recall. Finally, a structured way to evaluate the performance of supervised learning algorithms is proposed, as well as suggested which algorithm is best suitable for their data set by comparing the effectiveness of various algorithms.