A Novel Random Forest and its Application on Classification of Air Quality Air pollution has a serious impact on daily life. It is necessary to inform the air quality in time to the public in order to take measures in advance. Machine learning methods such as random forest are good at evaluating grades of air quality. We find the distribution of air data is imbalance, which leads to negative effect on random forest classifiers. We propose a random forest method based on samples grouped bootstrap to solve this problem. Then we design three sets of experiments to evaluate the performance of the proposed method. The results of experiments indicate that the proposed method presents an improvement of random forest when both apply on balance datasets. The improvement is very significant when they apply on imbalance datasets, where the new method is much better at classifying minority samples.

Experimenting Two Machine Learning Methods in Classifying River Water Quality Water is very important to human life. It is a vital aspect of human and ecosystem survival and health. As it affects human lives individually, the quality of water is a universal concern across the globe. When the quality of water deteriorates, the problem of water scarcity will follow. Water quality is highly dependent on various factors such as the increase of population, the rapid development of economic expansion, as well as environmental pollution. The objective of this study is to build a classification model for water quality. Two classification models are built by the WEKA data mining tool, which are the Random Forest algorithm and Random Tree algorithm. The performance of the model is measured based on accuracy, precision, and recall. The results showed that Random Forest gives a higher performance across all three evaluation metrics as compared to Random Tree algorithm. The results are hoped to assist the classification of water quality categories in different states and river locations across the world.