KARMA VERİ İKİLİLEŞTİRME YÖNTEMİ İLE YENİ BİR RASYONEL SINIFLANDIRMA YAKLAŞIMI

Year 2023, Volume: 11 Issue: 4, 1257 - 1269, 30.12.2023

Muhammed Sütçü , İbrahim Tümay Gülbahar

https://doi.org/10.21923/jesd.1121792

Abstract

Sınıflandırma algoritması, yeni gözlemlerin kategorisini belirlemek için kullanılan denetimli bir öğrenme tekniğidir. Ancak bazı durumlarda nicel ve nitel verilerin birlikte kullanılması gerekir. Bu yaklaşımla nicel ve nitel verilerin birlikte kullanılmasında karşılaşılan sorunlar aşılmaya çalışılmıştır. Bu çalışmada, gerçek dünyada veriler ikili, sayısal veya kategorik gibi farklı türlerde sınıflandırıldığından, tüm veri türlerini ikili verilere dönüştürerek yeni bir sınıflandırma tekniği modellenmektedir. Bu sayede çok özellikli veri sınıflandırma problemleri için daha doğru ve verimli bir karma veri ikilileştirme yaklaşımı geliştirilmiştir. Öncelikle mevcut veri setinden sınıfları belirlenmektedir ve ardından yeni önerilen veri ikilileştirme yaklaşımını kullanarak yeni örnekleri bu önceden belirlenmiş sınıflara sınıflandırılmaktadır. Bu algoritmanın her adımının nasıl verimli bir şekilde gerçekleştirilebileceğini sayısal bir örnekle gösterilmiştir. Ardından, önerilen yaklaşımı iyi bilinen bir iris veri kümesine uygulamış ve modelimiz önceki yaklaşımlara göre umut verici sonuçlar ve iyileştirmeler verdiği gösterilmiştir.

Keywords

İkilileştirme, Sınıflandırma, Veri Madenciliği, IRIS Veri Seti, Karar Modelleri.

A NEW RATIONAL CLASSIFICATION APPROACH BY THE NEW MIXED DATA BINARIZATION METHOD

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.

Keywords

Binarization, Classification, Data Mining, IRIS Data Set, Decision Models

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