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.

在过去十年中，我们看到空气污染水平发生了巨大变化，这已成为一个关键的环境问题。应谨慎处理，为专业医疗保健制定解决方案。只有正确分类数据，才能减少空气污染对人类健康的影响。在众多分类问题中，我们面临着类不平衡问题。从不平衡的数据中学习对研究人员来说始终是一项具有挑战性的任务，研究人员不时开发出可能的解决方案。在本文中，我们专注于以分类算法不会影响其性能的方式处理不平衡的类分布。所提出的算法基于调整核缩放（AKS）方法的概念来处理多类不平衡数据集。核函数的选择已经在加权标准和卡方检验的帮助下进行了评估。所有的实验评估都是在基于传感器的印度中央污染控制委员会 (CPCB) 数据集上进行的。所提出的算法以99.66%的最高准确率赢得了所有分类算法的比赛，即Adaboost（59.72%）、多层感知器（95.71%）、GaussianNB（80.87%）和SVM（96.92）。所提出算法的结果也优于现有文献方法。从这些结果中还可以清楚地看出，我们提出的算法对于处理类不平衡问题以及增强的性能是有效的。因此，