Abstract
In the last decade, we have seen drastic changes in the air pollution level, which has become a critical environmental issue. It should be handled carefully towards making the solutions for proficient healthcare. Reducing the impact of air pollution on human health is possible only if the data is correctly classified. In numerous classification problems, we are facing the class imbalance issue. Learning from imbalanced data is always a challenging task for researchers, and from time to time, possible solutions have been developed by researchers. In this paper, we are focused on dealing with the imbalanced class distribution in a way that the classification algorithm will not compromise its performance. The proposed algorithm is based on the concept of the adjusting kernel scaling (AKS) method to deal with the multi-class imbalanced dataset. The kernel function's selection has been evaluated with the help of weighting criteria and the chi-square test. All the experimental evaluation has been performed on sensor-based Indian Central Pollution Control Board (CPCB) dataset. The proposed algorithm with the highest accuracy of 99.66% wins the race among all the classification algorithms i.e. Adaboost (59.72%), Multi-Layer Perceptron (95.71%), GaussianNB (80.87%), and SVM (96.92). The results of the proposed algorithm are also better than the existing literature methods. It is also clear from these results that our proposed algorithm is efficient for dealing with class imbalance problems along with enhanced performance. Thus, accurate classification of air quality through our proposed algorithm will be useful for improving the existing preventive policies and will also help in enhancing the capabilities of effective emergency response in the worst pollution situation.
Highlights
In the machine learning paradigms, the classification of the new objects based on similar instances is one of the crucial tasks
If the new sample will come for classification, it will be classified into the majority class because the classifier has lower prediction accuracy toward the minority
We have proposed the support vector machine algorithm (SVM) classification, which has been integrated with the adjusting kernel scaling method
Summary
In the machine learning paradigms, the classification of the new objects based on similar instances is one of the crucial tasks. The classification task becomes more complicated when one of the classes contains fewer instances than the other class [1]. In the field of machine learning, it is one of the challenging tasks for classification algorithms to learn from imbalanced data. We are facing data imbalance issues in almost all the domains, or we can say that it is quite a common problem in all the fields. Class imbalance is one of the critical issues in machine learning paradigms. If the new sample will come for classification, it will be classified into the majority class because the classifier has lower prediction accuracy toward the minority
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