Feature Selection and Classification using a Positive Learning Approach Focused on Graph and Neural Network

Abstract

Real-world knowledge is represented by a knowledge graph that provides assistance for various applications built on the basis of artificial intelligence. Awareness of the neighbourhood is obtained from the individuals and relationships of the Knowledge Graph. High-dimensional data analysis is a difficult task in many applications and this article discusses the dimensionality by specifying a limited collection of features that implies high-dimensional data without visible or substantial data loss. An unsupervised learning approach based on learning that uses the neural network principle and learns the features using the graph. The Positive Feature Selection approach using the Neural Network (PFSNN) approach in this paper defines features using a graph where the classification is carried out by the NN process and analyses the output of the proposed system. The efficiency of the PFSNN is evaluated by contrasting it with existing classification methods and using different datasets. Performance is measured using the classification performance metrics and it is defined from the observation that the proposed PFSNN algorithm has the best outcome.