文章基本信息

标题：不均衡データからのランダムフォレストを利用した高速高次特徴抽出
本地全文：下载
作者：河合吉彦 ; 住吉英樹 ; 藤井真人等
期刊名称：映像情報メディア学会誌
印刷版ISSN：1342-6907
电子版ISSN：1881-6908
出版年度：2010
卷号：64
期号：12
页码：1951-1955
DOI：10.3169/itej.64.1951
出版社：The Institute of Image Information and Television Engineers
摘要：We propose a method of using a random forest algorithm to quickly detect semantically high-level features such as specific objects. The random forest has a lower computation cost than that of the common algorithm such as a support vector machine (SVM). However, it cannot cope with training data that have a large bias in the number of negative and positive examples. We improve the conventional training algorithm to ensure sampling the data with equal probability from each class when creating bootstrap samples, which increases the classification accuracy. Experiments on the Caltech-101 dataset resulted in a recall of 64.3% and precision of 71.1%, which were comparable to those of conventional methods. The average time needed for training and for detection were reduced to one sixteenth and one twenty-seventh that of SVM, respectively.
关键词：高次特徴抽出;不均衡学習データ;ランダムフォレスト