Anlaşılabilir Sınıflandırma Kurallarının Ayçiçeği Optimizasyon Algoritması ile Otomatik Keşfi

Year 2021, Volume: 10 Issue: 2, 233 - 241, 31.12.2021

Suna Yıldırım Güngör Yıldırım Bilal Alatas

https://doi.org/10.46810/tdfd.976397

Cited By: 3

Abstract

Kural madenciliği, veri madenciliğinin önemli alt dallarından biri olup günümüzde hala üzerinde çalışılan sıcak bir çalışma alanıdır. Nicel nitelik içeren veri setleri üzerinde çalışan standart sınıflandırma yöntemleri genellikle ön işlem aşamalarına ihtiyaç duyarlar. Bu yapılan ayrıklaştırmalar ise başarım kaybına yol açabilmektedir. Buna ek olarak standart sınıflandırma algoritmalarının kara-kutu yapılarından dolayı kural açıklanabilirlikleri iyi değildir. Bu noktada, sürekli veriler ile çalışabilen optimizasyon algoritmaları, bu dezavantajların üstesinden gelebilir. Bu çalışmada, son yılların başarılı optimizasyon algoritmalarından olan Ayçiçeği Optimizasyon algoritmasını kullanarak verimli bir kural madenciliği gerçekleştirilmiştir. Bunun için, farklı bir temsil biçimi kullanan aday bitki yapısı, bu optimizasyon algoritmasına uyarlanmıştır. Arama uzayı olarak üç farklı disipline ait veri seti kullanılmış ve yöntemin başarımını gözlemlemek için iyi bilinen beş farklı sınıflandırma algoritmasına ait sonuçlar paylaşılmıştır. Elde edilen sonuçlar, optimizasyon temelli yaklaşım ile veri setleri üzerinde herhangi bir ön işlem yapmaya gerek kalmadan açıklanabilir kurallar üretilebileceğini ispatlamaktadır.

Keywords

Optimizasyon, Kural madenciliği, Ayçiçeği optimizasyon algoritması, Doğruluk

Automatic Discovery of Comprehensible Classification Rules with Sunflower Optimization Algorithm

Abstract

Rule mining is one of the important sub-branches of data mining, and it is still a hot topic study area for researchers. Standard classification methods usually require pre-processing steps when working with datasets containing quantitative attributes. On the other hand, discretization at these stages may lead to a loss of performance and accuracy. In addition, due to the black-box nature of standard classification algorithms, rule explicability is not good. At this point, optimization algorithms that can work with continuous data can overcome these disadvantages. This study focuses on rule mining using the Sunflower Optimization algorithm, one of the successful optimization algorithms of recent years. For this, the candidate plant structure using a different representation format was adapted to this optimization algorithm. Data sets from three different disciplines were used as the search space, and the results of five different well-known classification algorithms were shared for performance observations. The results obtained proved that, with the optimization-based approach, explicable rules can be produced without any pre-processing on the data sets.

Keywords

Optimization, Rule Mining, Sunflower Optimization Algorithm, Accuracy

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