Scalable kernel-based SVM classification algorithm on imbalance air quality data for proficient healthcare

Abstract In the last decade, we have seen drastic changes in air pollution level, which has become a critical environmental issue. It should be handled carefully towards making solutions for proficient healthcare. Reducing impact of on human health is possible only if data correctly classified. numerous classification problems, are facing class imbalance Learning from imbalanced always challenging task researchers, and time to time, been developed by researchers. this paper, focused dealing with distribution way that algorithm will not compromise its performance. The proposed based concept adjusting kernel scaling (AKS) method deal multi-class dataset. function's selection evaluated help weighting criteria chi-square test. All experimental evaluation performed sensor-based Indian Central Pollution Control Board (CPCB) highest accuracy 99.66% wins race among all algorithms i.e. Adaboost (59.72%), Multi-Layer Perceptron (95.71%), GaussianNB (80.87%), SVM (96.92). results also better than existing literature methods. clear these our efficient problems along enhanced Thus, accurate quality through useful improving preventive policies enhancing capabilities effective emergency response worst situation.