Improved ID3 algorithm based on attribute values
Shuqin Tu · Jisuanji gongcheng yu sheji · 2008
ID3 is a classical decision tree induction algorithm in data mining.It has the preference bias in selecting attributes with multiple values and is related to the number of training examples.A new approach to solving these drawbacks is given.At first,the threshold of attributes value's number is assigned to optimize the decision tree in calculating the entropy.At the meantime,a tree pruning method is implemented by adopting another threshold to reduce the error rate of the fully expanded tree.Experimental results demonstrated that the improved ID3 algorithm(AVID3) is more efficient than the traditional ID3 algorithm on many data sets.