A SYSTEMATIC AND EFFICIENT INPUT SELECTION METHOD FOR ARTIFICIAL NEURAL NETWORKS USING MIXED-INTEGER NONLINEAR PROGRAMMING

Year 2022, Volume: 10 Issue: 3, 762 - 773, 01.09.2022

Hasan Şıldır , Erdal Aydın

https://doi.org/10.36306/konjes.1077177

Cited By: 1

Abstract

Selection of input variables of the empirical models has vital effect on the prediction performance, reduced overfitting and reduced computational load. Various trials and error and sequential methods in the literature to deal with input selection for artificial neural networks (ANNs). However, these methods are not considered as automatic and systematic. This study proposes a novel and efficient mixed integer nonlinear programming-based approach to handle optimal input selection and the ANN training simultaneously for classification problems. Such selection uses binary (0-1) variables to represent the presence of the input variables and trains traditional continuous network weights simultaneously. Two classification case studies are given to demonstrate the advantages by using widely used data sets and statistical measures. The first data set is related to the characterization of the type of a tumor related to breast cancer, the second data set is about predicting the type of a biotechnological product using different features, the last one is related to heart failure prediction. Results show that better test performance can be achieved with optimally selected inputs, resulting in reduced overfitting. The proposed approach delivers a significant advantage during the design and training of the ANNs and is also applicable to other empirical models.

Keywords

Input selection, Artificial neural networks, Mixed-integer programming

Supporting Institution

TÜBİTAK

Project Number

118C245

Thanks

This publication has been produced benefiting from the 2232 International Fellowship for Outstanding Researchers Program of TUBITAK (Project No: 118C245). However, the entire responsibility of the publication belongs to the owner of the publication.

Kesikli ve Sürekli Optimizasyon Kullanarak Yapay Sinir Ağları için Sistematik Girdi Seçimi Yöntemi

Abstract

Ampirik modellerin girdi değişkenlerinin seçimi, tahmin performansı, azaltılmış fazla uydurma ve hesaplama yükünün azaltılması üzerinde önemli etkiye sahiptir. Literatürde yapay sinir ağları (YSA) için girdi seçimi ile ilgili çeşitli deneme yanılma yöntemleri mevcuttur ancak bu metodlar sistematik ve otomatik olarak kabul edilmemektedir. Bu çalışma, sınıflandırma problemleri için optimal girdi seçimi ve YSA eğitimini aynı anda ele almak için yeni ve verimli bir karma tamsayılı doğrusal olmayan programlama tabanlı bir yaklaşım önermektedir. Bu seçim, girdi değişkenlerinin varlığını temsil etmek için ikili (0-1) değişkenleri kullanır ve geleneksel sürekli ağ ağırlıklarını veya parametrelerini aynı anda eğitir. Yaygın olarak kullanılan veri setleri ve istatistiksel ölçümler kullanarak avantajları göstermek amacıyla üç sınıflandırma vaka çalışması sunulmuştur. Birinci veri seti meme kanseri ile ilgili tümörün tipin-in karakterizasyonu ile ilgili olup, ikinci veri seti ise farklı özellikler kullanılarak bir biyoteknolojik ürünün tipinin tahmin edilmesi ile ilgilidir, son veri seti ise kalp sağlığı ile ilgilidir. Sonuçlar, optimal olarak seçilen girdiler ile düşük fazla uydurma sayesinde daha iyi test performansının elde edilebileceğini göstermektedir. Önerilen yaklaşım, YSA'ların tasarımı ve eğitimi sırasında önemli bir avantaj sağlar ve diğer ampirik modellere de uygulanabilir.

Keywords

Girdi Seçimi, Yapay Sinir Ağları, Kesikli ve sürekli optimizasyon

Project Number

118C245

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