SP-DDPT: a simple prescriptive-based domain data preprocessing technique to support multilabel-multicriteria learning with expert information

Abstract

ABSTRACT Computing is a consortium of machine learning techniques which works well and provides a sufficient information to make assessment from the chronological historic dataset. In real today a lot of machine analytic techniques available to explore and to investigate the depth knowledge of the dataset but fails to work with multicriteria. There are domains with multicriteria-based decisions like agricultural, spatial, medical, and etc., These domain dataset requires expert intervention as multicriteria to streamline the knowledge presented from the efficient machine learning techniques. The analysis should extend with prescription in order to make productive actions from the learning. This research work is proposed to support the prescription of dataset with a simple automated framework to express the expert percepted value. The simple prescriptive-based domain data preprocessing technique (SP-DDPT) algorithms were defined to support the universal criteria such as ‘less than,’ ‘more than’ and ‘between.’ The proposed framework was applied to the sample Indian coastal dataset and the expert percepted value was preprocessed and prescribed successfully.