Combination of multiple bipartite ranking for multipartite web content quality evaluation

Abstract

Web content quality evaluation is crucial to various web content processing applications. Bagging has a powerful classification capacity by combining multiple classifiers. In this study, similar to Bagging, multiple pairwise bipartite ranking learners are combined to solve the multipartite ranking problems for web content quality evaluation. Both encoding and decoding mechanisms are used to combine bipartite rankers to form a multipartite ranker and, hence, the multipartite ranker is called MultiRank.ED. Both binary encoding and ternary encoding extend each rank value to an L −1 dimensional vector for a ranking problem with L different rank values. Predefined weighting and adaptive weighting decoding mechanisms are used to combine the ranking results of bipartite rankers to obtain the final ranking results. In addition, some theoretical analyses of the encoding and the decoding strategies in the MultiRank.ED algorithm are provided. Computational experiments using the DC2010 datasets show that the combination of binary encoding and predefined weighting decoding yields the best performance in all four combinations. Furthermore, this combination performs better than the best winning method of the DC2010 competition.