Development of a hybrid artificial neural network - naive bayes classifier for binary classification problem of imbalanced datasets
This paper presents a hybrid approach that consists of two different methods from machine learning technique, which are the Artificial Neural Network (ANN) and Naïve Bayes. The proposed technique is purposely developed for classifying the two classes of imbalanced datasets. Architecture of ANN is ba...
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| Format: | Article |
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ICIC International
2011
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| _version_ | 1796856447924961280 |
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| author | Adam, Asrul Shapiai, Mohd. Ibrahim Ibrahim, Zuwairie Khalid, Marzuki Jau, Lee Wen |
| author_facet | Adam, Asrul Shapiai, Mohd. Ibrahim Ibrahim, Zuwairie Khalid, Marzuki Jau, Lee Wen |
| author_sort | Adam, Asrul |
| collection | ePrints |
| description | This paper presents a hybrid approach that consists of two different methods from machine learning technique, which are the Artificial Neural Network (ANN) and Naïve Bayes. The proposed technique is purposely developed for classifying the two classes of imbalanced datasets. Architecture of ANN is based on a single layer feedforward ANN for binary classification and the learning algorithm is assisted by the Particle Swarm Optimization (PSO) algorithm. As a main classifier, the Naïve Bayes is still being kept by using a conventional method. Consequently, by comparing with the individual classifiers that are used in this paper, the proposed approach is capable of improving the prediction performance that is evaluated by geometric mean (Gmean) as the performance measure. |
| first_indexed | 2024-03-05T18:43:06Z |
| format | Article |
| id | utm.eprints-28826 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T18:43:06Z |
| publishDate | 2011 |
| publisher | ICIC International |
| record_format | dspace |
| spelling | utm.eprints-288262019-01-28T03:38:29Z http://eprints.utm.my/28826/ Development of a hybrid artificial neural network - naive bayes classifier for binary classification problem of imbalanced datasets Adam, Asrul Shapiai, Mohd. Ibrahim Ibrahim, Zuwairie Khalid, Marzuki Jau, Lee Wen TK Electrical engineering. Electronics Nuclear engineering This paper presents a hybrid approach that consists of two different methods from machine learning technique, which are the Artificial Neural Network (ANN) and Naïve Bayes. The proposed technique is purposely developed for classifying the two classes of imbalanced datasets. Architecture of ANN is based on a single layer feedforward ANN for binary classification and the learning algorithm is assisted by the Particle Swarm Optimization (PSO) algorithm. As a main classifier, the Naïve Bayes is still being kept by using a conventional method. Consequently, by comparing with the individual classifiers that are used in this paper, the proposed approach is capable of improving the prediction performance that is evaluated by geometric mean (Gmean) as the performance measure. ICIC International 2011-09 Article PeerReviewed Adam, Asrul and Shapiai, Mohd. Ibrahim and Ibrahim, Zuwairie and Khalid, Marzuki and Jau, Lee Wen (2011) Development of a hybrid artificial neural network - naive bayes classifier for binary classification problem of imbalanced datasets. ICIC Express Letters, 5 (9A). pp. 3171-3175. ISSN 1881-803X http://www.ijicic.org/el-5(9)a.htm |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Adam, Asrul Shapiai, Mohd. Ibrahim Ibrahim, Zuwairie Khalid, Marzuki Jau, Lee Wen Development of a hybrid artificial neural network - naive bayes classifier for binary classification problem of imbalanced datasets |
| title | Development of a hybrid artificial neural network - naive bayes classifier for binary classification problem of imbalanced datasets |
| title_full | Development of a hybrid artificial neural network - naive bayes classifier for binary classification problem of imbalanced datasets |
| title_fullStr | Development of a hybrid artificial neural network - naive bayes classifier for binary classification problem of imbalanced datasets |
| title_full_unstemmed | Development of a hybrid artificial neural network - naive bayes classifier for binary classification problem of imbalanced datasets |
| title_short | Development of a hybrid artificial neural network - naive bayes classifier for binary classification problem of imbalanced datasets |
| title_sort | development of a hybrid artificial neural network naive bayes classifier for binary classification problem of imbalanced datasets |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
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