Attribute Reduction-Based Ensemble Rule Classifiers Method for Dataset Classification

Basir, Mohammad Aizat; Ahmad, Faudziah

doi:10.5121/csit.2017.70307

Cited by 1 publication

(2 citation statements)

References 10 publications

(6 reference statements)

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“…Ensemble learning algorithms are sets of classifiers or training data in which multiple models are trained to solve the same problem. The ensemble methods use multiple classifiers or different training sets to obtain a more accurate classification [47]. In this work, three ensemble learning algorithms, namely ensemble classifiers (DT, NB, and KNN), bagging, and AdaBoost, were used for the assessment of the prediction model.…”

Section: Evaluation Model With Ensemble Learning Methodsmentioning

confidence: 99%

“…Performance evaluation was performed through 10-fold cross validation, which is generally applied for classifying models and evaluation. The ensemble learning algorithms used multiple classifiers or different datasets to obtain a more accurate classification by combining many weak learners [47,48]. The ensemble approach for the classification problem is divided into two categories as follows:…”

Section: Evaluation Model With Ensemble Learning Methodsmentioning

confidence: 99%

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

Panthong

Srivihok

2019

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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.

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Section: Evaluation Model With Ensemble Learning Methodsmentioning

confidence: 99%

Section: Evaluation Model With Ensemble Learning Methodsmentioning

confidence: 99%