タイプ2ファジイ回帰分析に基づくSVMクラスタリングの構築
-
- 和多田 淳三
- 早稲田大学
-
- Yichen Wei
- 早稲田大学
-
- Witold Pedrycz
- アルバータ大学, カナダ
書誌事項
- タイトル別名
-
- Building a Qualitative Classification Model by Type-2 Fuzzy Regression Based Support Vector Machine
抄録
Methods of qualitative analysis such as qualitative classification have gained importance as an essential complement of existing quantitative analysis in numerous fields, such as behavior finance, econometrics, and business management. Only a few models have been developed to deal with qualitative inputs (attributes), which appear in the form of T2F data. Additionally, classification models are unsuitable if an output point is not fully assigned to a single class. In this paper, we formulate a comprehensive qualitative classification model based on fuzzy support vector machine (FSVM in brief) combined with Type-2 fuzzy expected regression (FER in brief) to deal with T2F inputs. This classifier(FER-FSVM in brief) makes it possible to achieve discrimination of output while characterizing membership for each class in terms of multi-dimensional qualitative inputs (attributes). Moreover, FER-FSVM can self-learn the data structure and shifted between FER or FSVM for classification automatically. It will largely shorten the computing time especially for large datasets by using linear structure of FER classifier to limit the size of non-linear classification region.
収録刊行物
-
- 日本知能情報ファジィ学会 ファジィ システム シンポジウム 講演論文集
-
日本知能情報ファジィ学会 ファジィ システム シンポジウム 講演論文集 30 (0), 470-475, 2014
日本知能情報ファジィ学会
- Tweet
キーワード
詳細情報 詳細情報について
-
- CRID
- 1390282680650828416
-
- NII論文ID
- 130005480520
-
- 本文言語コード
- ja
-
- データソース種別
-
- JaLC
- CiNii Articles
-
- 抄録ライセンスフラグ
- 使用不可