Morphometric Analysis of Craniodental Characters of the House Rat, Rattus rattus (Rodentia: Muridae) in Peninsular Malaysia

Ikbal, Nurul Huda Mohamad; Pathmanathan, Dharini; Bhassu, Subha; Simarani, Khanom; Omar, Hasmahzaiti

doi:10.17576/jsm-2019-4810-05

Cited by 1 publication

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Applying RFE-based features in the work done by Muhammad Ikbal et al (2019) may achieve more promising results to observe the significance difference of R. rattus age groups and these features could also be used in other conventional morphometric studies of rats to examine their morphological differences. This study can also be extended by using the same approach for the classification of different biological organisms to produce a more generalized model and consider the effect of multicollinearity in traditional morphometric features when applying RFE to improve classification accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…In Malaysia, studies related to R. rattus based on their craniodental measurements are still lacking especially on feature selection techniques to select the subset of the best morphometric features. This study aims to implement some machine learning approaches such as Naïve Bayes (NB), Random Forest (RF), and Artificial Neural Network (ANN) in aiding the feature selection process and achieving dimensionality reduction which can provide reliable age classification of R. rattus based on Muhammad Ikbal et al (2019).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Untitled

2023

JSM

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%