Ağırlıklı Sade Bayes Sınıflandırıcısında Etkili Sınıflandırma İçin Ağırlıkların Optimizasyonu

Abstract

Ağırlıklı sade bayes sınıflandırıcısı, sade bayes algoritmasına dayanan verimli bir sınıflandırma algoritmasıdır. Ancak bu algoritmada ağırlıkların belirlenmesi önemli bir problemdir. Izgara tarama yöntemi kullanılarak yapılan aramalarda genellikle en uygun çözüme ulaşılamamakta ve algoritma oldukça yavaş çalışmaktadır. Ağırlıkları hızlı bir şekilde bulmak için hızlı ağırlıklı sade bayes sınıflandırıcısı kullanılsa da bu algoritmanın performansı sınırlıdır. Bu nedenle, ağırlıkların optimize edilmesi hem zaman açısından hem de yüksek performansa ulaşmak açısından büyük önem taşımaktadır. Bu çalışmada, ağırlıklı sade bayes sınıflandırıcısının ağırlıklarını optimize etmek için genetik algoritma ve parçacık sürü optimizasyonu yöntemleri kullanılmıştır. Genetik Algoritma tabanlı Ağırlıklı Naive Bayes (GAW-NB) ve Parçacık Sürü Optimizasyonu tabanlı Ağırlıklı Naive Bayes (PSOW-NB) yöntemlerinin performansları, beş farklı veri kümesi üzerinde 5 katlı çapraz doğrulama testi ile değerlendirilmiştir. Yapılan deneylerin sonuçları, hem hız hem de sınıflandırma performansı açısından anlamlı sonuçlar ortaya koymuştur.

Keywords

• Ağırlıklı Sade Bayes
• Sınıflandırma
• Genetik Algoritma
• Parçacık Sürü Optimizasyonu.

Weight Optimization of Weighted Naive Bayes Classifier for Efficient Classification

Abstract

The Weighted Naive Bayes classifier is an efficient classification algorithm based on the Naive Bayes Algorithm. However, the determination of weights in this algorithm is an important problem. By using the Grid search method, the optimum solution is not reached and the algorithm works too slowly. Fast Weighted Naïve Bayes classifier is used to find weights quickly, but the performance of this algorithm is limited. Therefore, optimizing the weights has a great importance in terms of both time and achieving high performance. In this study, Genetic Algorithm and Particle Swarm Optimization methods were used to optimize the weights of the Weighted Naive Bayes classifier. The performance of Genetic Algorithm based Weighted Naive Bayes (GAW-NB) and Particle Swarm Optimization based Weighted Naive Bayes (PSOW-NB) methods were examined on five different sets with 5-fold cross validation testing method. The results of the experiments showed significant results both in terms of speed and classification performance.

Keywords

• Weighted Naive Bayes
• Classification
• Genetic Algorithm
• Particle Swarm Optimization.

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