• unsupervised feature selection with ensemble learning

    جزئیات بیشتر مقاله
    • تاریخ ارائه: 1392/07/24
    • تاریخ انتشار در تی پی بین: 1392/07/24
    • تعداد بازدید: 1091
    • تعداد پرسش و پاسخ ها: 0
    • شماره تماس دبیرخانه رویداد: -
     in this paper, we show that the way internal estimates are used to measure variable importance in random forests are also applicable to feature selection in unsupervised learning. we propose a new method called random cluster ensemble (rce for short), that estimates the out-of-bag feature importance from an ensemble of partitions. each partition is constructed using a different bootstrap sample and a random subset of the features. we provide empirical results on nineteen benchmark data sets indicating that rce, boosted with a recursive feature elimination scheme (rfe) (guyon and elisseeff, journal of machine learning research, 3:1157–1182, 2003), can lead to significant improvement in terms of clustering accuracy, over several state-of-the-art supervised and unsupervised algorithms, with a very limited subset of features. the method shows promise to deal with very large domains. all results, datasets and algorithms are available on line (http://perso.univ-lyon1.fr/haytham.elghazel/rce.zip).

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