ANALYSIS OF FEATURE SELECTION WITH CLASSFICATION: BREAST CANCER DATASETS

Classification, a data mining task is an effective method to classify the data in the process of Knowledge Data Discovery. A Classification method, Decision tree algorithms are widely used in medical field to classify the medical data for diagnosis. Feature Selection increases the accuracy of the Classifier because it eliminates irrelevant attributes. This paper analyzes the performance of Decision tree classifier-CART with and without feature selection in terms of accuracy, time to build a model and size of the tree on various Breast Cancer Datasets. The results show that a particular feature selection using CART has enhanced the classification accuracy of a particular dataset.

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