CAJ | 학술논문

Iterative Dichotomiser version3（ID3）算法是数据挖掘中经典的决策树分类算法,其核心是分裂训练集属性的选择标准,即分裂前后的信息增益量最大,用该标准选择属性时对于取值较多的属性具有较强依赖性。剖析了ID3算法存在的不足并加以改进,引入了属性关注度,提出了改进算法AAID3算法。实验表明改进算法对原ID3算法的取值偏向问题有所克服并使分类更加准确,决策树更加简明。
Iterative Dichotomiser version3（ID3）산법시수거알굴중경전적결책수분류산법,기핵심시분렬훈련집속성적선택표준,즉분렬전후적신식증익량최대,용해표준선택속성시대우취치교다적속성구유교강의뢰성。부석료ID3산법존재적불족병가이개진,인입료속성관주도,제출료개진산법AAID3산법。실험표명개진산법대원ID3산법적취치편향문제유소극복병사분류경가준학,결책수경가간명。
In decision tree sorting,Iterative Dichotomiser version 3（ID3） is a classical algorithm used to generate a decision tree invented by Ross Quinlan.Its core lies in the split-off training which lumped the standard of attributes preference,namely,information gain is of maximum quantity both before split-off and after split-off.The use of this preference standard to select attributes has a strong dependence in regard of the milti-valued attributes preference.Disadvantages of ID3 algorithm were analyzed and improved through introducing attribute attention.To the effect,AAID3 algorithm was proposed.Experimental results expatiated AAID3 algorithm is superior to the ID3 in accuracy of classification,concision of decision tree,and independence from multi-valued attributes.