Publications

Abstract : In dealing the typical issues associated with a high-dimensional information search space, the conventional streamlining algorithms off limits a sensible course of action in light of the fact that the interest space increases exponentially with the performance issue, along these lines handling these issues using exact techniques are not helpful. Without a doubt, the comparing data has demonstrated its strength as an indispensable advantage for the business elements and legislative association to take incite and consummate choices by methods for surveying the relevant records. As the number of features (attributes) expands, the computational cost of running the acceptance errand develops exponentially. This curse of dimensionality influences supervised and in addition unsupervised learning algorithms. The characteristics inside the informational collection may likewise be unimportant to the undertaking being contemplated, hence influencing the unwavering quality of the results. There might be a relationship between qualities in the informational index that may influence the execution of the order. In this way, a novel methodology known as ensemble classification algorithm is proposed in the view of the feature selection. We show that our algorithm compares favorably to existing algorithms, thus providing state of the art performance. This algorithm is proposed to lessen the computational overheads, adaptability, and information unbalancing in the Big Data.