Ayrıklaştırma Yöntemlerinin Karar ağaçlar ve Karar Kuralları Sınıflandırıcılar için Medikal Veri Setleri Üzerinde Karşılaştırılması

Year 2022, Issue: 35, 275 - 281, 07.05.2022

Yılmaz Kaya , Ramazan Tekin

https://doi.org/10.31590/ejosat.1080098

Abstract

Gerçek hayattaki veri kümeleri, veri tabanlarında reel sayılarla sunulmaktadır. Öte yandan, birliktelik kuralları ve tümevarım kuralları gibi birçok veri madenciliği yöntemi yalnızca ayrık öznitelikler gerektirirler. Bu nedenle sürekli özniteliklere sahip veri kümelerinin ayrık özniteliklere sahip veri kümelerine dönüştürülmesi gerekmektedir. Ayrıklaştırma işlemi, belirli bir sürekli öznitelik verisini aralıklara bölerek değer sayısını azaltmaktır. Bu çalışmada, kural ve ağaç tabanlı JRip, OneR, J48 ve Part sınıflandırıcı algoritmaları ile sekiz ayrıklaştırma yöntemi naliz edilmiştir. Denemeler, UCI veri deposundan alınan gerçek veri setlerinden oluşmakta ve on kat çapraz doğrulamayı sonuçlarını içermektedir. Bu algoritmaların sınıflandırma başarımı önemli ölçüde artırmada ayrıklaştırmanın önemli bir adım araç olduğunu görülmüştür. Son olarak, çalışma sonucunda PIMA, WBC ve DERMA veri setleri için sırasıyla MDL ve J48, CAIM ve Jrip ve Extended Chi ve J48 yöntemlerinin en yüksek doğruluğu verdiği görülmüştür.

Keywords

Sınıflandırma, Sürekli Öznitelikler, Ayrıklaştırma, Veri Madenciliği

Comparison of discretization methods for classifier decision trees and decision rules on medical data sets

Abstract

Data sets in real life are given by real numbers in databases. On the other hand, many data mining methods like association rules and induction rules require only discrete attributes. For this reason, it is necessary to convert data sets with continuous attributes into data sets with discrete attributes. Discretization process is reducing the number of values for a given continuous attribute by dividing the range of the attribute into intervals. In this paper, eight discretization methods are presented with JRip, OneR, J48, and Part classifier algorithms of rules and tress. Experiments include a ten-fold cross validation provided on the basis of real-life data sets from the UCI repository. We show that discretization is important step to significantly increase the classification results of these algorithms. Finally, as a result of the study, it was seen that MDL and J48, CAIM and Jrip and Extended Chi and J48 methods gave the highest accuracy for PIMA, WBC and DERMA data sets, respectively.

Keywords

Classification, Continuous attributes, Discretization, Data Mining

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