A novel undersampling method based on data classification method

Abstract

Data mining is one of the most important research area in literature. Due to the increasing volume of data, which is directly proportional to technological advancements, the number of researches in this field is growing rapidly. The goal of data mining is to extract various insights and obtain information from raw data by leveraging machine learning techniques. The structural characteristics and also class distributions of the datasets used in machine learning techniques significantly affect the performances of the algorithms. In this study, our aim is balancing the imbalanced binary dataset, used in the machine learning techniques, with an undersampling approach including a classification method via polyhedral conic functions.

Keywords

• Data mining
• machine learning
• undersamling
• polyhedral conic functions

Veri sınıflandırma yöntemine dayalı yeni bir alt örnekleme yöntemi

Abstract

Veri madenciliği literatürdeki en önemli araştırma alanlarından biridir. Teknolojik gelişmelerle doğru orantılı olarak artan veri hacmi nedeniyle bu alanda yapılan araştırmaların sayısı da hızla artmaktadır. Veri madenciliğinin amacı, makine öğrenimi tekniklerinden yararlanarak çeşitli tahminlerde bulunmak ve ham verilerden bilgi elde etmektir. Makine öğrenmesi tekniklerinde kullanılan veri kümelerinin yapısal özellikleri ve sınıf dağılımları algoritmaların performanslarını önemli ölçüde etkilemektedir. Bu çalışmada amacımız, dengesiz ikili veri kümelerini, çokyüzlü konik fonksiyonların kullanıldığı bir sınıflandırma yöntemini içeren yeni bir alt örnekleme yaklaşımıyla dengelemektir.

Keywords

• Veri madenciliği
• makine öğrenme
• alt örnekleme
• çokyüzlü konik fonksiyonlar.

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