Sains Malaysiana 52(7)(2023): 1901-1914

http://doi.org/10.17576/jsm-2023-5207-01

RFE-Based Feature Selection to Improve Classification Accuracy for Morphometric Analysis of Craniodental Characters of House Rats

(Pemilihan Ciri Berasaskan RFE untuk Meningkatkan Ketepatan Pengelasan dalam Analisis Morfometri Sifat Kraniodental Tikus Rumah)

ANEESHA BALACHANDRAN PILLAY¹, DHARINI PATHMANATHAN¹*, ARPAH ABU² & HASMAHZAITI OMAR²

¹Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur, Malaysia

²Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur, Malaysia

Received: 24 October 2022/Accepted: 26 June 2023

Abstract

In conventional morphometrics, researchers often collect and analyze data using large numbers of morphometric features to study the shape variation among biological organisms. Feature selection is a fundamental tool in machine learning which is used to remove irrelevant and redundant features. Recursive feature elimination (RFE) is a popular feature selection technique that reduces data dimensionality and helps in selecting the subset of attributes based on predictor importance ranking. In this study, we perform RFE on the craniodental measurements of the Rattus rattus data to select the best feature subset for both males and females. We also performed a comparative study based on three machine learning algorithms such as Naïve Bayes, Random Forest, and Artificial Neural Network by using all features and the RFE-selected features to classify the R. rattus sample based on the age groups. Artificial Neural Network has shown to provide the best accuracy among these three predictive classification models.

Keywords: ANN, machine learning, naïve Bayes, recursive feature elimination, traditional morphometrics

Abstrak

Dalam morfometri konvensional, para penyelidik sering mengumpul dan menganalisis data dengan menggunakan bilangan ciri yang besar untuk mengkaji variasi bentuk antara organisma biologi. Pemilihan ciri memainkan peranan penting dalam pembelajaran mesin algorithma untuk mengeluarkan ciri-ciri yang tidak relevan dan berlebihan. Penghapusan ciri rekursif (RFE) merupakan kaedah pemilihan ciri terkenal yang boleh mengurangkan dimensi data dan juga boleh membantu memilih subset sifat berdasarkan kedudukan kepentingan peramal. Dalam kajian ini, kita menjalankan RFE pada ukuran kraniodental linear bagi data Rattus rattus untuk memilih subset ciri terbaik bagi kedua-dua tikus jantan dan betina. Kita telah menjalankan kajian perbandingan berdasarkan tiga algoritma pembelajaran mesin seperti Bayes Naif, Hutan Rawak dan Rangkaian Neural Tiruan menggunakan semua ciri dan ciri terpilih secara RFE untuk mengelaskan sampel R. rattus berdasarkan kumpulan umur. Setelah memantau hasil nilai ketepatan yang diperoleh bagi ketiga-tiga modal tersebut, Rangkaian Neural Tiruan telah terbukti memberi ketepatan yang terbaik antara ketiga-tiga model ini.

Kata kunci: ANN; Bayes naif; morfometri tradisi; pembelajaran mesin; penghapusan ciri rekursif

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*Corresponding author; email: dharini@um.edu.my