Pearson Correlation Coefficient Based Attribute Weighted k-NN for Air Pollution Prediction
Ankita Jain, Rajya Lakshmi Lella · 2020
This paper proposes a formula based on Pearson Correlation Coefficient for the calculation of weights of attributes. These weights are used in a version of k-Nearest Neighbor (k-NN) method called Attribute Weighted k-NN with the aim to achieve better performance than simple k-NN and Distance Weighted k-NN by eliminating the dominating effect of irrelevant attributes while calculating the distance between instances. The proposed formula of weights computation in Attribute Weighted k-NN also performs better than when weights are assigned the same as Pearson Correlation Coefficient values. Weights computed using the proposed formula decrease the Root Mean Square Error (RMSE) by 9.26%, 2.63% and 2.51% and increase the R-Squared by 1.36%, 0.25% and 0.2% for NO, NO2and NOX, respectively, in comparison to the weights assigned the same as Pearson Correlation Coefficient values in Attribute Weighted k-NN. This paper has also been able to reduce the execution time of Attribute Weighted k-NN and make it near to that of simple k-NN and Distance Weighted k-NN. Before applying k-NN, data preprocessing steps like Data Cleaning (Missing Values and Noisy Data Removal) and Attribute Reduction are applied to the data to transform it into an efficient and useful format.