Self-adaptive attribute weighting for Naive Bayes classification
•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....
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| Published in: | Expert systems with applications Vol. 42; no. 3; pp. 1487 - 1502 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Amsterdam
Elsevier Ltd
15.02.2015
Elsevier |
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| ISSN: | 0957-4174, 1873-6793 |
| Online Access: | Get full text |
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| Abstract | •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.
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. |
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| AbstractList | 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. •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. 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. |
| Author | Wu, Jia Pan, Shirui Zhang, Chengqi Cai, Zhihua Zhang, Peng Zhu, Xingquan |
| Author_xml | – sequence: 1 givenname: Jia surname: Wu fullname: Wu, Jia email: jia.wu@student.uts.edu.au organization: School of Computer Science, China University of Geosciences, Wuhan 430074, China – sequence: 2 givenname: Shirui surname: Pan fullname: Pan, Shirui email: shirui.pan@student.uts.edu.au organization: Quantum Computation & Intelligent Systems (QCIS) Centre, Faculty of Engineering & Information Technology, University of Technology Sydney, NSW 2007, Australia – sequence: 3 givenname: Xingquan surname: Zhu fullname: Zhu, Xingquan email: xzhu3@fau.edu organization: Department of Computer & Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA – sequence: 4 givenname: Zhihua surname: Cai fullname: Cai, Zhihua email: zhcai@cug.edu.cn organization: School of Computer Science, China University of Geosciences, Wuhan 430074, China – sequence: 5 givenname: Peng surname: Zhang fullname: Zhang, Peng email: peng.zhang@uts.edu.au organization: Quantum Computation & Intelligent Systems (QCIS) Centre, Faculty of Engineering & Information Technology, University of Technology Sydney, NSW 2007, Australia – sequence: 6 givenname: Chengqi surname: Zhang fullname: Zhang, Chengqi email: chengqi.zhang@uts.edu.au organization: Quantum Computation & Intelligent Systems (QCIS) Centre, Faculty of Engineering & Information Technology, University of Technology Sydney, NSW 2007, Australia |
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| Keywords | Self-adaptive Evolutionary computing Naive Bayes Artificial Immune Systems Attribute weighting Initialization Artificial life Image processing Multicriteria analysis Evolutionary algorithm Adaptability Conditional probability Probabilistic reasoning Immunity Modeling Adaptive method Data specification Efficiency Selection criterion Probability learning Immune system Bayes estimation Local search Computer vision Data analysis Probabilistic approach Image databank Independence Text Multidimensional database Supervised learning Learning (artificial intelligence) Artificial intelligence Image classification |
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| Snippet | •Self-adaptive attribute weighting for Naive Bayes classification.•Using Artificial Immune Systems (AIS) for attribute weighting.•Seamlessly integrating... Naive Bayes (NB) is a popular machine learning tool for classification, due to its simplicity, high computational efficiency, and good classification accuracy,... |
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| SubjectTerms | Accuracy Algorithmics. Computability. Computer arithmetics Applied sciences Artificial Immune Systems Artificial intelligence Attribute weighting Bayesian analysis Classification Computer science; control theory; systems Data processing. List processing. Character string processing Evolutionary computing Exact sciences and technology Learning Learning and adaptive systems Machine learning Memory organisation. Data processing Naive Bayes Pattern recognition. Digital image processing. Computational geometry Self-adaptive Software Theoretical computing Weighting |
| Title | Self-adaptive attribute weighting for Naive Bayes classification |
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