Concepts Seeds Gathering and Dataset Updating Algorithm for Handling Concept Drift

Concepts Seeds Gathering and Dataset Updating Algorithm for Handling Concept Drift

Nabil M. Hewahi, Ibrahim M. Elbouhissi
Copyright: © 2015 |Pages: 29
DOI: 10.4018/IJDSST.2015040103
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Abstract

In data mining, the phenomenon of change in data distribution over time is known as concept drift. In this research, the authors introduce a new approach called Concepts Seeds Gathering and Dataset Updating algorithm (CSG-DU) that gives the traditional classification models the ability to adapt and cope with concept drift as time passes. CSG-DU is concerned with discovering new concepts in data stream and aims to increase the classification accuracy using any classification model when changes occur in the underlying concepts. The proposed approach has been tested using synthetic and real datasets. The experiments conducted show that after applying the authors' approach, the classification accuracy increased from low values to high and acceptable ones. Finally, a comparison study between CSG-DU and Set Formation for Delayed Labeling algorithm (SFDL) has been conducted; SFDL is an approach that handles sudden and gradual concept drift. CSG-DU results outperforms SFDL in terms of classification accuracy.
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Žliobaitė [45] conducted a survey of concept drift problem. She introduced a taxonomy for adaptive supervised techniques. This taxonomy describes in details the main contributions on adaptive supervised learning techniques. In Žliobaitė's taxonomy, there are two types of contributions introduced:

  • 1.

    Learners with Triggers: Determine how the models or sampling should be changed at a given time.

  • 2.

    Evolving Learners: Find ways to keep the base learner updated with every change happen.

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