An improved Id3 algorithm for medical data classification
作者： Shuo Yang , Jing-Zhi Guo , Jun-Wei Jin
DOI: 10.1016/J.COMPELECENG.2017.08.005
关键词: Artificial intelligence 、 Word error rate 、 Computer science 、 Data classification 、 C4.5 algorithm 、 Classifier (UML) 、 Decision stump 、 ID3 algorithm 、 Statistical classification 、 Random tree 、 Machine learning 、 Data mining
摘要: Abstract Data mining techniques play an important role in clinical decision making, which provides physicians with accurate, reliable and quick predictions through building different models. This paper presents improved classification approach for the prediction of diseases based on classical Iterative Dichotomiser 3 (Id3) algorithm. The Id3 algorithm overcomes multi-value bias problem when selecting test/split attributes, solves issue numeric attribute discretization stores classifier model form rules by using a heuristic strategy easy understanding memory savings. Experiment results show that is superior to current four algorithms (J48, Decision Stump, Random Tree Id3) terms accuracy, stability minor error rate.
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