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Liver Cancer Classification Model Using Hybrid Feature Selection Based on Class-Dependent Technique for the Central Region of Thailand

Computer Science Department, Faculty of Science, Kasetsart University, 50 Ngam Wong Wan Rd., Bangkok 10900, Thailand
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Information 2019, 10(6), 187; https://doi.org/10.3390/info10060187
Received: 23 April 2019 / Revised: 24 May 2019 / Accepted: 29 May 2019 / Published: 31 May 2019
(This article belongs to the Section Artificial Intelligence)
Liver cancer data always consist of a large number of multidimensional datasets. A dataset that has huge features and multiple classes may be irrelevant to the pattern classification in machine learning. Hence, feature selection improves the performance of the classification model to achieve maximum classification accuracy. The aims of the present study were to find the best feature subset and to evaluate the classification performance of the predictive model. This paper proposed a hybrid feature selection approach by combining information gain and sequential forward selection based on the class-dependent technique (IGSFS-CD) for the liver cancer classification model. Two different classifiers (decision tree and naïve Bayes) were used to evaluate feature subsets. The liver cancer datasets were obtained from the Cancer Hospital Thailand database. Three ensemble methods (ensemble classifiers, bagging, and AdaBoost) were applied to improve the performance of classification. The IGSFS-CD method provided good accuracy of 78.36% (sensitivity 0.7841 and specificity 0.9159) on LC_dataset-1. In addition, LC_dataset II delivered the best performance with an accuracy of 84.82% (sensitivity 0.8481 and specificity 0.9437). The IGSFS-CD method achieved better classification performance compared to the class-independent method. Furthermore, the best feature subset selection could help reduce the complexity of the predictive model. View Full-Text
Keywords: hybrid feature selection; liver cancer; class-dependent; ensemble learning; multi-class hybrid feature selection; liver cancer; class-dependent; ensemble learning; multi-class
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Panthong, R.; Srivihok, A. Liver Cancer Classification Model Using Hybrid Feature Selection Based on Class-Dependent Technique for the Central Region of Thailand. Information 2019, 10, 187.

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