Efficient Knowledge Transformation for Incremental Learning and Detection of New Concept Class in Students Classification System

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 340)

Abstract

The efficient knowledge transformation with the concept class detection is an important challenge for the incremental learning system, where the student’s data is flowing continuously. The massive amount of raw student’s data in the education system can be transformed into the information and buried knowledge can be taken out of it for the purpose of offering good career choice to the students or for the purpose of detection of student’s performance. The algorithm proposed in the paper learn continuous raw data, transform previous knowledge to the current data without referring to the old data and able to efficiently accommodate new concept class detected by the system. The proposed system is applied to the student’s classification problem for detecting new students samples introduced to the system. In this paper four classifiers are used as a base classifier and for the updating of weight distribution randomly for efficient knowledge transformation and dynamically consult vote strategy is used for detection of new concept class. Simulation results over student’s data are used to validate the efficiency of the proposed method.

Keywords

Incremental learning Knowledge transformation Concept class 

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Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, Sipna College of Engineering and TechnologySGBAUAmravatiIndia

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