Improving the Performance of the Decision Boundary Making Algorithm via Outlier Detection

• Item type: Journal(1)
• 公開日: 2015-07-15
• タイトル: Improving the Performance of the Decision Boundary Making Algorithm via Outlier Detection
• タイトル / 言語: en
• タイトル: Improving the Performance of the Decision Boundary Making Algorithm via Outlier Detection
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [一般論文（推薦論文）] decision boundary making, support vector machine, neural network, outlier detection
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: School of Computer Science and Engineering, University of Aizu
• 著者所属: School of Computer Science and Engineering, University of Aizu
• 著者所属: School of Computer Science and Engineering, University of Aizu
• 著者所属: School of Computer Science and Engineering, University of Aizu
• 著者所属(英): en
• 著者所属(英): School of Computer Science and Engineering, University of Aizu
• 著者所属(英): en
• 著者所属(英): School of Computer Science and Engineering, University of Aizu
• 著者所属(英): en
• 著者所属(英): School of Computer Science and Engineering, University of Aizu
• 著者所属(英): en
• 著者所属(英): School of Computer Science and Engineering, University of Aizu
• 著者名: Yuya, Kaneda; Yan, Pei; Qiangfu, Zhao; Yong, Liu
• 著者名(英): Yuya, Kaneda; Yan, Pei; Qiangfu, Zhao; Yong, Liu
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Outlier detection is one of the methods for improving the performance of machine learning models. Since outliers often affect the performance of the learning models negatively, it is desired to detect and remove outliers before model construction. In this paper, we try to improve the performance of the decision boundary making (DBM) algorithm via outlier detection. DBM has been proposed by us for inducing compact and high performance learning models that are suitable for implementation in portable computing devices. The basic idea of DBM is to generate data that can fit the decision boundary (DB) of a high performance model, and then induce a compact model based on the generated data. In our study, a support vector machine (SVM) is used as the high performance model, and a single hidden layer multilayer perceptron (MLP) is used as the compact model. Experimental results obtained so far show that DBM performs well in many cases, but its performance still is not good enough for some applications. In this paper, we use SVM not only for obtaining the DB, but also for detecting the outliers, so that better MLP can be induced using cleaner data. We use a threshold δoutlier to control the number of outliers to remove. Experimental results show that, if we select δoutlier properly, the DBM incorporated with outlier detection outperforms the original DBM, and it is better than or comparable to SVM for all databases used in the experiments.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.23(2015) No.4 (online); DOI http://dx.doi.org/10.2197/ipsjjip.23.497; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Outlier detection is one of the methods for improving the performance of machine learning models. Since outliers often affect the performance of the learning models negatively, it is desired to detect and remove outliers before model construction. In this paper, we try to improve the performance of the decision boundary making (DBM) algorithm via outlier detection. DBM has been proposed by us for inducing compact and high performance learning models that are suitable for implementation in portable computing devices. The basic idea of DBM is to generate data that can fit the decision boundary (DB) of a high performance model, and then induce a compact model based on the generated data. In our study, a support vector machine (SVM) is used as the high performance model, and a single hidden layer multilayer perceptron (MLP) is used as the compact model. Experimental results obtained so far show that DBM performs well in many cases, but its performance still is not good enough for some applications. In this paper, we use SVM not only for obtaining the DB, but also for detecting the outliers, so that better MLP can be induced using cleaner data. We use a threshold δoutlier to control the number of outliers to remove. Experimental results show that, if we select δoutlier properly, the DBM incorporated with outlier detection outperforms the original DBM, and it is better than or comparable to SVM for all databases used in the experiments.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.23(2015) No.4 (online); DOI http://dx.doi.org/10.2197/ipsjjip.23.497; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 56, 号 7, 発行日 2015-07-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764