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Subject: search.filters.subject.ID3 algorithm

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    Improving the ID3 Algorithm By Filtering Out Attributes With Values Of 0 or 1
    (2022-01-01)
    Chuenprasertsuk, Pinyarat
    ;
    Jearanaitanakij, Kietikul
    The iterative Dichotomiser 3 (ID3) algorithm is a classification algorithm that generates a decision tree, and it is one of the most simple and well-known tools. However, there is still room for improvement. This paper aims to reduce the running time of generating a decision tree by ignoring attributes that have an information gain value of 0 or 1 and the experiment result shows that the improved ID3 algorithm can significantly reduce the running time by more than 10 percent when compared with the traditional ID3 algorithm.
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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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    Improving ID3 Algorithm by Using A∗ Search
    (2018-08-21)
    Kaewrod, Nicha
    ;
    Jearanaitanakij, Kietikul
    ID3 is one of the most widely used algorithms for creating a classification decision tree. However, the traditional ID3 algorithm has a difficulty when there are equally important attributes during the decision node construction. It randomly selects one of the most important attributes to serve as the current decision node. This behavior may lead to the resulting decision tree which contains unnecessary depths and suboptimal accuracy. The purpose of this paper is to find the near optimal depth decision tree of the dataset, which contains an equally important attributes problem, by using the A-Star (A∗) search. The experiment results on four standard datasets from UCI indicate that the proposed algorithm can significantly reduce the decision tree's depth and still maintain the classification accuracy.
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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.
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    Improving ID3 algorithm by ignoring minor instances
    (2018-07-02)
    Kaewrod, Nicha
    ;
    Jearanaitanakij, Kietikul
    Among various classification algorithms, ID3 is one of the most widely used and well-known tools that generates an efficient decision tree. Nevertheless, ID3 is too rigorous in generating the decision rules. As a result, the final decision tree may carry too many decision rules. Some of these decision rules may have very low number of instances which do not make significant change to the classification accuracy. The aim of this paper is to propose an approach to relax the rigorousness of the conventional ID3 algorithm by ignoring minor instances so that the resulting decision tree will have the lower number of depths yet produce promising accuracy. The proposed algorithm is examined on six datasets from UCI repository and Weka. The experimental results indicate that the proposed algorithm not only significantly reduces the maximum number of depths of the decision tree, but also retains the classification accuracy in the satisfying level. Moreover, the training time, the classification time, and the number of decision rules of the proposed algorithm are lower than those of the conventional ID3.