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Author: search.filters.author.Kraidech, SuratchananSubject: search.filters.subject.Decision tree

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    Improving ID3 Algorithm by Combining Values from Equally Important Attributes
    (2018-08-21)
    Kraidech, Suratchanan
    ;
    Jearanaitanakij, Kietikul
    ID3 is a well-known algorithm which is used in the classification task of the decision tree learning. Although a lot of research provides improvements on the traditional ID3 algorithm with various strategies, no attempt was made on ID3 to address the problem when there are more than one attribute that can be placed at a particular node, i.e. those attributes are equally important. This paper proposes a new variation of ID3 to combine equally important attributes into a single node of the decision tree classification. The Connect-4 dataset from UCI is used in our experiment since the dataset contains many attributes and instances which can easily encounter the equally important attribute problem. The experimental results show that our proposed method significantly reduces the average depth of the decision tree generated by ID3 algorithm while the average accuracy rate is still preserved.
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    Reducing the depth of ID3 algorithm by combining values from neighboring important attributes
    (2018-07-02)
    Kraidech, Suratchanan
    ;
    Jearanaitanakij, Kietikul
    The ID3 algorithm is one of the most popular decision tree algorithms which is mainly used in the classification task. There are many pieces of research about improving the ID3 algorithm by using various strategies. We improved the ID3 algorithm by addressing the equally important attributes problem in our previous work. In this paper, we extend our previous algorithm by changing the attribute selection to allow the neighboring second-place important attributes to be combined with the most important attributes. The proposed algorithm is tested on four standard benchmarks from the UCI repository. The experimental results indicate the significant reduction in the maximum depth and the classification depth of the decision tree. In addition, the testing time is also reduced while the classification accuracy is satisfying stable.