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A Directed Acyclic Graph (DAG) Ensemble Classification Model: An Alternative Architecture for Hierarchical Classification

A Directed Acyclic Graph (DAG) Ensemble Classification Model: An Alternative Architecture for Hierarchical Classification

Esra'a Alshdaifat, Frans Coenen, Keith Dures
Copyright: © 2017 |Volume: 13 |Issue: 3 |Pages: 18
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781522511335|DOI: 10.4018/IJDWM.2017070104
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MLA

Alshdaifat, Esra'a, et al. "A Directed Acyclic Graph (DAG) Ensemble Classification Model: An Alternative Architecture for Hierarchical Classification." IJDWM vol.13, no.3 2017: pp.73-90. http://doi.org/10.4018/IJDWM.2017070104

APA

Alshdaifat, E., Coenen, F., & Dures, K. (2017). A Directed Acyclic Graph (DAG) Ensemble Classification Model: An Alternative Architecture for Hierarchical Classification. International Journal of Data Warehousing and Mining (IJDWM), 13(3), 73-90. http://doi.org/10.4018/IJDWM.2017070104

Chicago

Alshdaifat, Esra'a, Frans Coenen, and Keith Dures. "A Directed Acyclic Graph (DAG) Ensemble Classification Model: An Alternative Architecture for Hierarchical Classification," International Journal of Data Warehousing and Mining (IJDWM) 13, no.3: 73-90. http://doi.org/10.4018/IJDWM.2017070104

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Abstract

In this paper, a hierarchical ensemble classification approach that utilizes a Directed Acyclic Graph (DAG) structure is proposed as a solution to the multi-class classification problem. Two main DAG structures are considered: (i) rooted DAG, and (ii) non-rooted DAG. The main challenges that are considered in this paper are: (i) the successive misclassification issue associated with hierarchical classification, and (i) identification of the starting node within the non-rooted DAG approach. To address these issues the idea is to utilize Bayesian probability values to: select the best starting DAG node, and to dictate whether single or multiple paths should be followed within the DAG structure. The reported experimental results indicated that the proposed DAG structure is more effective than when using a simple binary tree structure for generating a hierarchical classification model.

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