Affiliation:
1. ABDULLAH GÜL ÜNİVERSİTESİ
2. ABDULLAH GUL UNIVERSITY
Abstract
Classification algorithm is a supervised learning technique that is used to identify the category of new observations. However, in some cases, quantitative and qualitative data must be used together. With this approach, we tried to overcome the problems encountered in using quantitative and qualitative data together. In this paper, we model a new classification technique by converting all types of data to binary data because in real world, data are classified in different types such as binary, numeric or categorical. By this way, we develop a more accurate and efficient mixed data binarization approach for multi-attribute data classification problems. First, we determine the classes from available dataset and then we classify the new instances into these predetermined classes by using the new proposed data binarization approach. We show how each step of this algorithm could be performed efficiently with a numeric example. Then, we apply the proposed approach on a well-known iris dataset and our model show promising results and improvements over previous approaches.
Publisher
Muhendislik Bilimleri ve Tasarim Dergisi
Subject
Colloid and Surface Chemistry,Physical and Theoretical Chemistry
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