International Journal of Applied Mathematics and Machine LearningAuthor's: Md. Siraj-Ud-Doulah and Md. Ashad Alam
Pages: [15] - [45]
Received Date: February 17, 2019; Revised February 23, 2019
Submitted by: Jianqiang Gao.
DOI: http://dx.doi.org/10.18642/ijamml_7100122032
Classification is a key supervised machine learning technique, which is essential and interesting topic for ecological research. It deals a way to classify a dataset into subsets that share common designs. Particularly, there are many classification processes to select from, each creating firm assumptions about the data and about how classification should be grouped. In this paper, we applied eight machine learning classification algorithms such as Decision Trees, Random Forest, Artificial Neural Network, Support Vector Machine, Linear Discriminant Analysis, k-nearest neighbours, Logistic Regression and Naive Bayes on ecological data. The objective of this study is to compare different machine learning classification algorithms in ecological dataset. In this analysis we have checked the accuracy test among the algorithms. In our study we conclude that linear discriminant analysis and k-nearest neighbours are the best methods.
machine learning, classification algorithms, sensitivity, accuracy, F-score, ecological dataset.
