Magpie and Composition Features Comparison using Classification Algorithms on Superconductivity Dataset using WEKA

Аннотация

Data mining extracts the useful information by analysis of the large data sets by grouping the data and then targeting the particular points to mgenerate useful information. Techniques of data mining are used in various fields for example medical sciences, business, education, industries. These techniques involves classification, clustering, association, prediction etc. The purpose of this paper is to evaluate the efficacy of two features Magpie and One-hot composition on super conductivity datasets. This study explored the data of 2876 instance samples using WEKA by applying Linear Regression, Random Forest, and MLP as classification algorithms.