Comparative Accuracy of Different Classification Algorithms for Forest Cover Type Prediction

Abstract

Machine learning based classifiers used quite often for predicting forest cover types, are the Naïve Bayes classifier, the k-Nearest Neighbors classifier, and the Random forest classifier. This paper is directed towards examining all of these classifiers coupled with feature selection and attribute derivation in order to evaluate which one is best suited for forest cover type classification. Numerous training classifications were performed on each of the classifiers with different sets of features. Amongst the three classifiers evaluated in this work, the Random Forest classifier is exhibiting the best and highest accuracy over others. Feature selection also played a significant role in demonstrating the accuracy levels obtained in each of the classifiers.