Optimized weights of document keywords for auto-reply accuracy

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• 作者：Jinn-Tsong Tsai
• 关键词：Keyword weight ; Auto-reply accuracy ; e-learning ; Differential evolution ; Taguchi method
• 刊名：Neurocomputing
• 出版年：2014
• 出版时间：26 January, 2014
• 年：2014
• 卷：124
• 期：Complete
• 页码：43-56
• 全文大小：359 K

文摘

A Taguchi-crossover differential evolution (TCDE) algorithm is proposed to optimize weights of document keywords for auto-reply accuracy. The proposed TCDE algorithm combines the use of differential evolution for exploring the optimal feasible region in macro-space with the use of the Taguchi method for exploiting the optimal solution in micro-space. For learning purpose, an answer needs to be exactly given for a specific query. Notably, teachers give a problem answer to elementary students who need to have the clear and accurate solution for learning according to their queries. This study shows the TCDE which integrates a cosine similarity measure and an evaluation function to successfully find the best weights of document keywords for auto-reply accuracy. Performance comparisons confirm that the TCDE algorithm outperforms existing methods presented in the literature in finding the best weights of document keywords and obtaining accurate answers.