Research and realization of improved native Bayes classification algorithm under big data environment
Chu Zhang · Journal of Beijing Jiaotong University · 2015
Native Bayes classification algorithm is a simple and efficient classification algorithm.However,its application is partly restricted because the assumptions of conditional independence are difficult to satisfy in reality.A modified Bayes classification algorithm with attribute weights is put forward based on association rules and confidence to solve this problem.Different weights are provided for different attributes based on association rules and confidence.The proposed algorithm is implemented using MapReduce programming mode,thus the classification performance of native Bayes classification algorithm improves effectively while maintains its simplicity.Experiments in EMU maintenance show that the method indeed improves the accuracy and efficiency of classification algorithms.