CAJ | 학술논문

机器学习分类领域提出大量的分类算法，如何为数据集找到合适的分类算法成为研究的重要内容之一。文献[8]提出一种新的数据集离散化方法用来刻画数据集的特征，且在推荐方法方面取得较好的结果。本文在此基础上利用交互信息理论刻画数据集的属性与属性及属性与类标签之间协作关系，提出基于二变量和基于三变量的交互信息特征结构。通过12种分类算法在UCI数据库中的98个数据集上的性能实验，结果表明与文献[8]的方法相比，两种方法都能明显提高推荐方法的精度和命中率，且对于适应性较差的数据集，基于三变量的交互信息方法更为有效。
궤기학습분류영역제출대량적분류산법，여하위수거집조도합괄적분류산법성위연구적중요내용지일。문헌[8]제출일충신적수거집리산화방법용래각화수거집적특정，차재추천방법방면취득교호적결과。본문재차기출상이용교호신식이론각화수거집적속성여속성급속성여류표첨지간협작관계，제출기우이변량화기우삼변량적교호신식특정결구。통과12충분류산법재UCI수거고중적98개수거집상적성능실험，결과표명여문헌[8]적방법상비，량충방법도능명현제고추천방법적정도화명중솔，차대우괄응성교차적수거집，기우삼변량적교호신식방법경위유효。
In machine learning area, classification algorithms are widely studied and a large number of different types of algorithms are proposed. How to select appropriate ones from so many classification algorithms for the datasets becomes a crucial problem. Recently, a new method in reference [8] is proposed to characterize datasets and achieve better results in algorithm recommendation. In this paper, two methods are presented to characterize datasets under the theory of interaction information. The performance of 12 different types of classification algorithms on the 98 UCI datasets illustrates that both two-variable and three-variable interaction information methods can improve the precision and the hit rate of recommended algorithms. Furthermore, the latter performs even better under datasets with poor adaptability.