A Study Based on Distributed Supervised Machine Learning System for Text Classification
Complex data confronts the centralized supervised machine learning system (CSMLS) with some embarrassments in performance, self-adaptability and scalability for text classification. In this paper, aiming to resolve these embarrassments, a novel distributed supervised machine learning system (DSMLS) model is proposed. Based on data distribution consistency, classifier performance, and evidence belief we fuse predicted information from these diverse classification agents in DSMLS. It is experimentally shown that DSMLS provides better performance than CSMLS. Compared with CSMLS, maximally DSMLS reduces 21.5% in training time and improves 8.4% in F1.
KeywordsClassifier Performance Combination Rule Evidence Theory Basic Probability Assignment Machine Learning Research
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