Ecological Data Analysis Based on Machine Learning Algorithms

TL;DR

This paper compares eight machine learning classification algorithms on ecological data to determine which methods yield the highest accuracy, highlighting Linear Discriminant Analysis and k-nearest neighbors as top performers.

Contribution

It provides a comparative analysis of multiple machine learning classifiers specifically applied to ecological datasets, identifying the most effective methods.

Findings

Linear Discriminant Analysis achieved high accuracy.

k-nearest neighbors outperformed other classifiers.

Decision Trees and Random Forest also showed competitive results.

Abstract

Classification is an important supervised machine learning method, which is necessary and challenging issue for ecological research. It offers a way to classify a dataset into subsets that share common patterns. Notably, there are many classification algorithms to choose from, each making certain assumptions about the data and about how classification should be formed. 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 neighbors, Logistic Regression and Naive Bayes on ecological data. The goal 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 neighbors are the best methods among all other methods