• Journal of KIISE:Software and Applications
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• v.34 no.9
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• pp.804-816
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• 2007

Sample training data for machine learning often contain irrelevant information or redundant concept. It is also the case that the original data may include noise. If the information collected for constructing learning model is not reliable, it is difficult to obtain accurate information. So the system attempts to find relations or regulations between features and categories in the teaming phase. The feature selection is to remove irrelevant or redundant information before constructing teaming model. for improving its performance. Existing feature selection methods assume that the distribution of documents is balanced in terms of the number of documents for each class and the length of each document. In practice, however, it is difficult not only to prepare a set of documents with almost equal length, but also to define a number of classes with fixed number of document elements. In this paper, we propose a new feature selection method that considers the impurities among the words and unbalanced distribution of documents in categories. We could obtain feature candidates using the word impurity and eventually select the features through unbalanced distribution of documents. We demonstrate that our method performs better than other existing methods via some experiments.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.679-681
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• 2005

효과적인 문서 분류를 위해 학습 하고자 하는 클래스와 관련된 많은 특징들이 필요하다. 하지만 학습하고자 하는 개념과 관련이 없거나 중복된 정보가 수집된 정보 속에 존재한다. 학습 과정에서 정확한 지식 습득을 하기 위해 특징 선택 방법을 사용하였다. 본 논문에서는 클래스에 대한 단어의 불순도를 이용한 특징 선택 방법을 제안한다. 기존의 특징 선택 방법과 비교 분석하여 기존 특징 선택 방법의 문제점을 파악하고 개선된 기법을 보인다.