Kianmehr Keivan, Alhajj Reda
BIDEALS Group, Department of Computer Science, University of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada T2N 1N4.
Artif Intell Med. 2008 Sep;44(1):7-25. doi: 10.1016/j.artmed.2008.05.002. Epub 2008 Jun 30.
In this study, we aim at building a classification framework, namely the CARSVM model, which integrates association rule mining and support vector machine (SVM). The goal is to benefit from advantages of both, the discriminative knowledge represented by class association rules and the classification power of the SVM algorithm, to construct an efficient and accurate classifier model that improves the interpretability problem of SVM as a traditional machine learning technique and overcomes the efficiency issues of associative classification algorithms.
In our proposed framework: instead of using the original training set, a set of rule-based feature vectors, which are generated based on the discriminative ability of class association rules over the training samples, are presented to the learning component of the SVM algorithm. We show that rule-based feature vectors present a high-qualified source of discrimination knowledge that can impact substantially the prediction power of SVM and associative classification techniques. They provide users with more conveniences in terms of understandability and interpretability as well.
We have used four datasets from UCI ML repository to evaluate the performance of the developed system in comparison with five well-known existing classification methods. Because of the importance and popularity of gene expression analysis as real world application of the classification model, we present an extension of CARSVM combined with feature selection to be applied to gene expression data. Then, we describe how this combination will provide biologists with an efficient and understandable classifier model. The reported test results and their biological interpretation demonstrate the applicability, efficiency and effectiveness of the proposed model.
From the results, it can be concluded that a considerable increase in classification accuracy can be obtained when the rule-based feature vectors are integrated in the learning process of the SVM algorithm. In the context of applicability, according to the results obtained from gene expression analysis, we can conclude that the CARSVM system can be utilized in a variety of real world applications with some adjustments.
在本研究中，我们旨在构建一个分类框架，即CARSVM模型，该模型集成了关联规则挖掘和支持向量机（SVM）。目标是利用两者的优势，即类关联规则所代表的判别知识和SVM算法的分类能力，构建一个高效且准确的分类器模型，以改善SVM作为传统机器学习技术时的可解释性问题，并克服关联分类算法的效率问题。
在我们提出的框架中：不是使用原始训练集，而是将一组基于类关联规则对训练样本的判别能力生成的基于规则的特征向量呈现给SVM算法的学习组件。我们表明，基于规则的特征向量呈现出高质量的判别知识源，这可以极大地影响SVM和关联分类技术的预测能力。它们在可理解性和可解释性方面也为用户提供了更多便利。
我们使用了来自UCI机器学习库的四个数据集，与五种著名的现有分类方法相比，评估所开发系统的性能。由于基因表达分析作为分类模型的实际应用的重要性和普遍性，我们提出了结合特征选择的CARSVM扩展，以应用于基因表达数据。然后，我们描述了这种结合将如何为生物学家提供一个高效且可理解的分类器模型。报告的测试结果及其生物学解释证明了所提出模型的适用性、效率和有效性。
从结果可以得出结论，当将基于规则特征向量集成到SVM算法的学习过程中时，可以显著提高分类准确率。在适用性方面，根据从基因表达分析中获得的结果，我们可以得出结论，CARSVM系统经过一些调整后可用于各种实际应用。
