In machine learning, the data imbalance imposes challenges to perform data analytics in almost all areas of real-world research. The raw primary data often suffers from the skewed perspective of data distribution of one class over the other as in the case of computer vision, information security, marketing, and medical science. The goal of this article is to present a comparative analysis of the approaches from the reference of data pre-processing, algorithmic and hybrid paradigms for contemporary imbalance data analysis techniques, and their comparative study in lieu of different data distribution and their application areas.

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Benzene is a carcinogen and employing hardware sensors to detect its concentration is expensive, along with limited operational efficiency. There is a relation among various atmospheric gas concentrations and therefore, some heuristic regression approaches can be applied for benzene forecasting, if given the concentration level of other gases. This paper proposes a new adaptive benzene prediction model using an improved particle swarm optimization (PSO) based adaptive neuro fuzzy inference system (ANFIS). Improved PSO enhances the performance of ANFIS by considering the multiobjective fitness function involving accuracy, root mean squared error (RMSE), and coefficient of determination (r 2 ). The proposed technique has been tested on both publicly available air quality datasets and a real world dataset of Patiala City in India. Extensive analysis reveals that the proposed technique outperforms other state-of-the-art techniques, making it well suited for building effective and economical benzene prediction models.

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