Efficiency rectification of predictive machine learning models by data centric optimization

Abstract

There are two ways to optimize the performance of machine learning models, one is model centric, and another is data centric. Model centric approaches sometimes cannot yield expected results, so researchers and developers are now focusing on data centric optimization. Main steps of data centric approaches are – noise detection and correction, effective feature selection, feature transformation and data augmentation in case of limited datasets. Be it image, audio, text or numerical data, the mentioned approaches are always beneficial irrespective of model architecture. This paper is devoted to developing new preprocessing techniques for numerical data. The developed algorithm is tested with different artificial and real datasets for linear regression, k-means clustering, SVM classifier and deep learning networks. The algorithm is easy to implement and computationally less expensive. Experimental results show that proposed methods work well to improve model efficiency.