Development and evaluation of M + 1-way classification mechanism realized through identifying foreign patterns

• Published in: Soft computing (Berlin, Germany) Vol. 27; no. 8; pp. 4659 - 4668
• Main Authors: Zhang, Huimin, Zhu, Xiubin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2023
• Subjects: Artificial Intelligence; Computational Intelligence; Control; Data Analytics and Machine Learning; Engineering; Mathematical Logic and Foundations; Mechatronics; Robotics; Information granule; Classification with rejection; Three-way decision; Granular computing; Foreign pattern
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-022-07737-x

In this study, we are concerned with a new design methodology of M  + 1-way classification mechanism. The intent is to reduce the cost of error prediction caused by insufficient evidence. The study is motivated by the notion of three-way decisions, which has been successfully used in various application areas to build human-centric systems. In contrast to traditional multiple classifications, one additional class is added into the proposed architecture to represent the reject decision made on foreign patterns, which exhibit significant differences compared to the patterns used for constructing the classification models. A collection of information granules is constructed on the basis of available experimental evidence to form a compact and interpretable representation of the feature space occupied by the native patterns. The patterns located outside the regions occupied by these information granules are identified and filtered out prior to classification while only the native patterns are subject to classification. The proposed methodology leads to a human-centric and human-interactive construct in which the rejected patterns need further processing. Different distance functions are utilized in the construction of information granules. The performance of the proposed architecture is evaluated involving one synthetic dataset and a collection of publicly available datasets.