Exploiting domain knowledge to address class imbalance and a heterogeneous feature space in multi-class classification
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Published:2023-02-27
Issue:5
Volume:32
Page:1037-1064
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ISSN:1066-8888
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Container-title:The VLDB Journal
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language:en
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Short-container-title:The VLDB Journal
Author:
Hirsch Vitali,Reimann Peter,Treder-Tschechlov Dennis,Schwarz Holger,Mitschang Bernhard
Abstract
AbstractReal-world data of multi-class classification tasks often show complex data characteristics that lead to a reduced classification performance. Major analytical challenges are a high degree of multi-class imbalance within data and a heterogeneous feature space, which increases the number and complexity of class patterns. Existing solutions to classification or data pre-processing only address one of these two challenges in isolation. We propose a novel classification approach that explicitly addresses both challenges of multi-class imbalance and heterogeneous feature space together. As main contribution, this approach exploits domain knowledge in terms of a taxonomy to systematically prepare the training data. Based on an experimental evaluation on both real-world data and several synthetically generated data sets, we show that our approach outperforms any other classification technique in terms of accuracy. Furthermore, it entails considerable practical benefits in real-world use cases, e.g., it reduces rework required in the area of product quality control.
Funder
Deutsche Forschungsgemeinschaft Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg
Publisher
Springer Science and Business Media LLC
Subject
Hardware and Architecture,Information Systems
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