Handling of Class Imbalanced Problem in Big Data Sets: An Experimental Evaluation (UCPMOT) - Patil, SS; Sonavane, SP (International Journal of Computer Sciences and Engineering, 2025)
The huge amount of NoSQL data has acknowledged a new provision of context for processing. A new trail of data handling technologies with massive resources assists to store and process these gigantic data sets. The current attention is to determine the undisclosed information by assimilating this data bulks & handling it as per use. Further they are pre-processed and converted for needful analysis. The volume and variety of these data sets endure rising relentlessly. Moreover, imbalanced in many real-worlds vast data sets have elevated a point of concern in the research domain. The skewed distribution of classes in the data sets poses a difficulty to learn using traditional classifiers. They tend more towards majority classes. In recent years, numerous solutions have been proposed to address imbalanced classification. However, they fail to address the various data characteristics such as overlapping, redundancy involving classification performance. A rational over_sampling technique i.e. Updated Class Purity Maximization Over_Sampling Technique using Safe-Level based synthetic sample creation is proposed to efficiently handle imbalanced data sets. The newly suggested Lowest versus Highest method addresses the handling of multi-class data sets. The data sets from the UCI repository are processed using the mapreduce based programming on Hadoop framework. The evaluation parameters viz. F-measure and AUC are used to authenticate the performance of proposed technique over benchmarking techniques. The results attained evidently quote the dominance of the proposed technique.