Feature Selection in Data Mining using Permutation Combination Article Swipe

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c4.5 algorithm computer science naive bayes classifier feature selection data mining decision tree permutation (music) classifier (uml) machine learning field (mathematics) artificial intelligence bayes' theorem support vector machine mathematics bayesian probability physics pure mathematics acoustics

Fifie Francis , J. S. Saleema ·

YOU? · · 2017 · Open Access · · DOI: https://doi.org/10.26483/ijarcs.v8i3.2952 · OA: W2955886598

The identification of useful patterns from a medical dataset benefits early diagnosis. Classification techniques in data mining are famous for medical data prediction. Decision Tree (J48) and Navie Bayes are the most popular and frequently used algorithms in the field of prediction analysis. The efficiency of such prediction algorithms can be improved with better pre-processing approaches. This paper aims at finding optimum features using permutation combination of input data attributes for improving the classifier accuracy. The Pima Indian Diabetes Dataset from UCI repository is used for experimentation. The performance of J48 and Navie Bayes has been tested for different combination of features.