Abstract

Rule induction (RI) is one of the known classification approaches in data mining. RI extracts hidden patterns from instances in terms of rules. This paper proposes a logic-based rule induction (LBRI) classifier based on a switching function approach. LBRI generates binary rules by using a novel minimization function, which depends on simple and powerful bitwise operations. Initially, LBRI generates instance codes by encoding the dataset with standard binary code and then generates prime cubes (PC) for all classes from the instance codes by the proposed reduced offset method. Finally, LBRI selects the most effective PC of the current classes and adds them into the binary rule set that belongs to the current class. Each binary rule represents an If–Then rule for the rule induction classifiers. The proposed LBRI classifier is based on basic logic functions. It is a simple and effective method, and it can be used by intelligent systems to solve real-life classification/prediction problems in areas such as health care, online/financial banking, image/voice recognition, and bioinformatics. The performance of the proposed algorithm is compared to six rule induction algorithms; decision table, Ripper, C4.5, REPTree, OneR, and ICRM by using nineteen different datasets. The experimental results show that the proposed algorithm yields better classification accuracy than the other rule induction algorithms on ten out of nineteen datasets.

中文翻译：

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数据挖掘分类问题的一种新型切换函数方法

摘要

规则归纳（RI）是数据挖掘中已知的分类方法之一。RI根据规则从实例中提取隐藏模式。本文提出了一种逻辑-基于规则感应（LBRI）的基础上的分类器开关功能的方法。LBRI通过使用新颖的最小化函数生成二进制规则，该函数依赖于简单而强大的按位运算。最初，LBRI通过使用标准二进制代码对数据集进行编码来生成实例代码，然后生成素数立方体（PC）从实例代码的所有类通过建议的减少偏移量方法。最后，LBRI选择当前类别中最有效的PC，并将它们添加到属于当前类别的二进制规则集中。每个二进制规则代表规则归纳分类器的If – Then规则。提出的LBRI分类器基于基本逻辑功能。这是一种简单有效的方法，可被智能系统用于解决诸如医疗保健，在线/金融银行，图像/语音识别和生物信息学等领域的现实分类/预测问题。将该算法的性能与六种规则归纳算法进行了比较。决策表，Ripper，C4.5，REPTree，OneR和ICRM，方法是使用19个不同的数据集。实验结果表明，在19个数据集中，有10个数据集比其他规则归纳算法具有更好的分类精度。