CLASSIFICATION OF WINE DATASET USING EFFICIENT MODIFIED RANDOM FOREST ALGORITHM

K. Ammulu

Abstract


Data mining is used in many applications to provide best solutions. The researchers on data mining invented various solutions to predict data. Random forest algorithm is a one of the ensemble learning method for classification and regression of dataset. It is not accurate and facing classification problem. In this paper, we propose modified random forest algorithm to merge random forest algorithm and particle swarm optimization algorithm. To analyze the performance of proposed method, wine data with 13 attributes is taken as an input from UCI repository, confusion matrix is used to measure accuracy, recall, F1, and precision. Compare with random forest algorithm proposed method exhibit better results.

 

Keywords: Data Mining, Classification, Random Forest Algorithm, Wine Dataset, Prediction


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