Self-adaptive attribute weighting for Naive Bayes classification. Issue 3 (15th February 2015)

• Record Type: Journal Article
• Authors: Wu, Jia; Pan, Shirui; Zhu, Xingquan; Cai, Zhihua; Zhang, Peng; Zhang, Chengqi
• Is Part Of: Expert systems with applications. Volume 42:Issue 3(2015)
• Page Start: 1487
• Page End: 1502
• Publication Date: 2015-02-15
• Subjects: Naive Bayes -- Self-adaptive -- Attribute weighting -- Artificial Immune Systems -- Evolutionary computing; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2014.09.019
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Self-adaptive attribute weighting for Naive Bayes classification. Using Artificial Immune Systems (AIS) for attribute weighting. Seamlessly integrating learning objective and AIS affinity function for attribute weighting. Experiments on 42 real-world datasets demonstrating superb performance gain. Abstract: Naive Bayes (NB) is a popular machine learning tool for classification, due to its simplicity, high computational efficiency, and good classification accuracy, especially for high dimensional data such as texts. In reality, the pronounced advantage of NB is often challenged by the strong conditional independence assumption between attributes, which may deteriorate the classification performance. Accordingly, numerous efforts have been made to improve NB, by using approaches such as structure extension, attribute selection, attribute weighting, instance weighting, local learning and so on. In this paper, we propose a new Artificial Immune System (AIS) based self-adaptive attribute weighting method for Naive Bayes classification. The proposed method, namely AISWNB, uses immunity theory in Artificial Immune Systems to search optimal attribute weight values, where self-adjusted weight values will alleviate the conditional independence assumption and help calculate the conditional probability in an accurate way. One noticeable advantage of AISWNB is that the unique immune system based evolutionary computation process, including initialization, clone, section, and mutation, ensures that AISWNB can adjust itself to the data without explicit specification of functional or distributional forms of the underlying model. As a result, AISWNB can obtain good attribute weight values during the learning process. Experiments and comparisons on 36 machine learning benchmark data sets and six image classification data sets demonstrate that AISWNB significantly outperforms its peers in classification accuracy, class probability estimation, and class ranking performance.