An Improved Classifier Chain Algorithm for Multi-label Classification of Big Data Analysis
The widely known classifier chains method for multi-label classification, which is based on the binary relevance (BR) method, overcomes the disadvantages of BR and achieves higher predictive performance, but still retains important advantages of BR, most importantly low time complexity. Nevertheless, despite its advantages, it is clear that a randomly arranged chain can be poorly ordered. We overcome this issue with a different strategy: Several times K-means algorithms are employed to get the correlations between labels and to confirm the order of binary classifiers. The algorithm ensure the right correlations be transmitted persistently as great as possible by improve the earlier predictions accuracy. The experimental results on the Reuters-21578 text chat data set and image data set show that the approach is efficient and appealing in most cases.