Handling incomplete quantitative data for supervised learning based on fuzzy entropy | IEEE Conference Publication | IEEE Xplore

Handling incomplete quantitative data for supervised learning based on fuzzy entropy


Abstract:

In recent years, machine learning and knowledge discovery techniques have attracted a great deal of attention in the information area. Classification is one of the import...Show More

Abstract:

In recent years, machine learning and knowledge discovery techniques have attracted a great deal of attention in the information area. Classification is one of the important research topics on these research areas. Most of the researches on classification concern that a complete data set is given as a training set and the test data know all values of attributes clearly. Unfortunately, incomplete data are commonly seen in real-world applications. In this paper, we propose a new strategy to deal with the incomplete quantitative data and introduce a supervised learning method based on genetic programming to handle the classification problem with incomplete data in the attributes. Two experiments are designed to evaluate the effectiveness of the proposed approaches.
Date of Conference: 25-27 July 2005
Date Added to IEEE Xplore: 05 December 2005
Print ISBN:0-7803-9017-2
Conference Location: Beijing, China

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