کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
4944114 1363983 2018 17 صفحه PDF ندارد دانلود رایگان
عنوان انگلیسی مقاله
Heterogeneous classifier ensemble with fuzzy rule-based meta learner
ترجمه فارسی عنوان
گروه طبقه بندی ناهمگن با متا یادگیرنده مبتنی بر قاعده فازی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

•We propose a heterogeneous ensemble system with a fuzzy IF-THEN rule-based meta learner.•The proposed system is highly competitive to several well-known machine learning algorithms, ensemble methods, and fuzzy IF-THEN rule-based classification systems.

In heterogeneous ensemble systems, each learning algorithm learns a classifier on a given training set to describe the relationship between a feature vector and its class label. As each classifier outputs different result on an observation, uncertainty is introduced. In this paper, we introduce a heterogeneous ensemble system with a fuzzy IF-THEN rule inference engine as the combiner to capture the uncertainty in the outputs of the base classifiers. In our method, fuzzy rules are generated on the outputs of an ensemble of base classifiers, which can be viewed as the class posterior probability of the observations. The performance of our method was evaluated on thirty datasets and in comparison with nine ensemble methods (AdaBoost, Decision Template, Decision Tree on meta-data, and six fixed combiners) and two single learning algorithms (SVM with L2-loss function and Decision Tree), and was shown to significantly outperforms these algorithms in terms of classification accuracy.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 422, January 2018, Pages 144-160
نویسندگان
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