However, in the classification process, both major and minor class samples play a significant role. Hence, the overall performance of machine-learning algorithms is affected, thereby generating inaccurate results. As a result of this uneven distribution of data samples, major class is given higher importance than minor class. In this case, the higher data sample class becomes the major class and the class consisting of relatively less data samples is labeled as minor class. This scenario in a data set where samples of data in one class are much higher in comparison to that of the other class is represented as a skewed data set. As a result, generation of data occurs in a skewed way. Many times, it is observed that such massive data samples are unevenly matched and classified among its classes. Recently, enormous data is aggregated on a daily basis. The performance of classification is evaluated with various performance metrics to determine the efficiency of classification. Here, three different sampling techniques such as Resampling, SpreadSubSampling, and SMOTE are implemented to reduce this uneven data distribution issue and classified with the K-nearest neighbor algorithm.

The paper stresses upon data preprocessing using sampling techniques to overcome the data-skewing problem. Thus, the data-skewing issue is one of the critical problems that need attention of researchers. While most algorithms focus on major data samples, they ignore the minor class data. Due to the uneven distribution of data among classes, data classification with machine-learning techniques has become more tedious. With a constant surge of the amount of data gathered from different sources, efficient processing and analysis of data is becoming difficult. In the past few years, classification has undergone some major evolution. 3Division of Information & Communication, Baekseok University, ChePonan-si, South Korea.2Department of Computer Science and Engineering, Siksha ‘O' Anusandhan Deemed to be University, Bhubaneswar, India.1School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar, India.Sushruta Mishra 1 *, Pradeep Kumar Mallick 1, Lambodar Jena 2 and Gyoo-Soo Chae 3

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