Ecological Data Analysis Based on Machine Learning Algorithms
This work provides a comparative analysis of classification methods for ecological researchers, but it is incremental as it applies existing algorithms without new methodological contributions.
The study compared eight machine learning classification algorithms on ecological data to identify the best performers, finding that Linear Discriminant Analysis and k-nearest neighbors achieved the highest accuracy.
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