Performance Analysis of a Parameter Selection Model on a Big Data Set - Ch. Indira Priyadarsini, Eluri Nageswara Rao, Kalluri Siva Krishna, Nangineni Srikanth, K Ravi kiran Yasaswi, Popuri Srinivasarao, Adusumalli Balaji (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Among the numerous phases within the data analysis process, the meticulous choice of parameters or attributes stands out as a pivotal stage. An erroneous choice in this regard can lead to suboptimal decisions. In the process of decision analysis, it proves advantageous for the decision-maker to have the capability to select and employ the most suitable model for identifying the optimally configured attribute set. In recent times, a substantial number of data scientists across various application domains have been attracted to the exploration of the advantages and disadvantages associated with big data. One prominent challenge arises when there is no appropriate model available to serve as a guiding framework, making the evaluation of extensive and diverse data in a big data environment particularly daunting for data scientists. Consequently, this study proposes an alternative parameterization approach capable of yielding an optimal attribute set while minimizing the associated learning, utilization, and maintenance costs. This model is constructed by integrating two complementary models with the soft set theory, best-first search algorithm, correlation-based feature selection, and rough set theory, all working synergistically as a parameter selection methodology. The proposed model has notably emerged as a strong contender in experiments focused on processing vast datasets.