Feature Selection Using Different Mutual Information Estimation Methods

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2010Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2010Bu çalışmada, farklı karşılıklı bilgi kestirim yöntemlerinin öznitelik seçimi üzerindeki etkisi incelenmiş, minimum-bolluk-maksimum-ilgi (mRMR) ve karşılıklı bilgi filtresi öznitelik seçim yöntemleri, bölümlemeden daha gelişmiş kestirim yöntemleri olan çekirdek yoğunluk kestirimi (KDE) bazlı ve k en yakın komşu (KNN) bazlı yöntemler kullanılarak iyileştirilmeye çalışılmıştır. Ayrıca bu karşılıklı bilgi kestirim yöntemlerinin yapay ve gerçek veriler üzerindeki başarımı ölçülmüş ve yöntemlerin başarımı altküme seçimi ve birleştirme yolları ile arttırılmaya çalışılmıştır. Altküme seçimi ve birleştirme yöntemlerinin başarımı arttırmadığı, k en yakın komşu bazlı kestirim yönteminin karşılıklı bilgi filtresi için kullanıldığında bölümlemeden daha yüksek başarım sağladığı, fakat mRMR’ın bundan yararlanamadığı görülmüştür.In this study, effect of different mutual information estimation methods on feature selection is examined, minimum-redundancy-maximum-relevance and mutual information filter feature selection methods are tried to be improved by using more advanced mutual information estimation methods than binning like k-nearest-neighbour (KNN) based and kernel density estimation (KDE) based methods. Besides, performances of these mutual information estimation methods on artificial and real data are measured and this performance is tried to be improved by subset selection and combination. It is concluded that subset selection and combination does not improve performance, KNN based estimation method improves performance when used in mutual information filter but mRMR does not benefit from this.Yüksek LisansM.Sc

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