Exploring Text Classification Configurations - A Bottom-up Approach to Customize Text Classifiers based on the Visualization of Performance

Alejandro Gabriel Villanueva Zacarias, Laura Kassner, Bernhard Mitschang

Abstract

Automated Text Classification (ATC) is an important technique to support industry expert workers, e.g. in product quality assessment based on part failure reports. In order to be useful, ATC classifiers must entail reasonable costs for a certain accuracy level and processing time. However, there is little clarity on how to customize the composing elements of a classifier for this purpose. In this paper we highlight the need to configure an ATC classifier considering the properties of the algorithm and the dataset at hand. In this context, we develop three contributions: (1) the notion of ATC Configuration to arrange the relevant design choices to build an ATC classifier, (2) a Feature Selection technique named Smart Feature Selection, and (3) a visualization technique, called ATCC Performance Cube, to translate the technical configuration aspects into a performance visualization. With the help of this Cube, business decision-makers can easily understand the performance and cost variability that different ATC Configurations have in their specific application scenarios.

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Paper Citation


in Harvard Style

Villanueva Zacarias A., Kassner L. and Mitschang B. (2017). Exploring Text Classification Configurations - A Bottom-up Approach to Customize Text Classifiers based on the Visualization of Performance . In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-247-9, pages 504-511. DOI: 10.5220/0006309705040511


in Bibtex Style

@conference{iceis17,
author={Alejandro Gabriel Villanueva Zacarias and Laura Kassner and Bernhard Mitschang},
title={Exploring Text Classification Configurations - A Bottom-up Approach to Customize Text Classifiers based on the Visualization of Performance},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2017},
pages={504-511},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006309705040511},
isbn={978-989-758-247-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Exploring Text Classification Configurations - A Bottom-up Approach to Customize Text Classifiers based on the Visualization of Performance
SN - 978-989-758-247-9
AU - Villanueva Zacarias A.
AU - Kassner L.
AU - Mitschang B.
PY - 2017
SP - 504
EP - 511
DO - 10.5220/0006309705040511