PREDICTION AND FEATURE REDUCTION USING NON PARAMETRIC DATA MINING TECHNIQUES
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Abstract
Dimensionality Reduction is a technique that endeavors to convert the data from high dimensional space to a less dimensional space while holding measurements among them and further promotes the accuracy. Data mining has great potential in healthcare field. In this paper different data mining classification techniques like k-Nearest Neighbor, Support Vector Machine, Random Forest, and Principal Component Analysis have been implemented. This paper deals with Attribute selection for Dimensionality reduction in Machine learning. The experimental results are tabulated and graphs indicate the performance of each of the technique used. The Support Vector Machine provides better results with highest accuracy and least error rate, when compared with other classifiers.
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