Bipartite weighted graph access for optimal label prediction

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Abstract

This article portrayed a novel bipartite weighted graph strategy for Feature optimization for machine learning models. Unlike many of the existing optimization techniques of diverse categories such as evolutionary computation techniques, diversity assessment strategies, the proposal is deterministic approach with minimal computational overhead, which has referred further as Bipartite Weighted Graph Approach for Optimal label prediction (BWG-OLP). The proposed model is about to derive a given feature is optimal or not by the respective feature’s correlation with the records and the correlation with the fellow features. The experimental study has carried on benchmark datasets to estimate the significance of the proposed method.

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Puligadda, J. (2019). Bipartite weighted graph access for optimal label prediction. International Journal of Recent Technology and Engineering, 8(2 Special Issue 8), 1848–1852. https://doi.org/10.35940/ijrte.B1167.0882S819

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