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Exploring the Hidden Challenges Associated with the Evaluation of Multi-class Datasets using Multiple Classifiers

Iram, S, Al-Jumeily, D, Fergus, P and Hussain, A (2014) Exploring the Hidden Challenges Associated with the Evaluation of Multi-class Datasets using Multiple Classifiers. In: 2014 EIGHTH INTERNATIONAL CONFERENCE ON COMPLEX, INTELLIGENT AND SOFTWARE INTENSIVE SYSTEMS (CISIS), . (8th International Conference on Complex, Intelligent and Software Intensive Systems (CISIS), 02 July 2014 - 04 July 2014, Birmingham City Univ, Birmingham, UNITED KINGDOM).

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The optimization and evaluation of a pattern recognition system requires different problems like multi-class and imbalanced datasets be addressed. This paper presents the classification of multi-class datasets which present more challenges when compare to binary class datasets in machine learning. Furthermore, it argues that the performance evaluation of a classification model for multi-class imbalanced datasets in terms of simple “accuracy rate” can possibly provide misleading results. Other parameters such as failure avoidance, true identification of positive and negative instances of a class and class discrimination are also very important. We, in this paper, hypothesize that “misclassification of true positive patterns should not necessarily be categorized as false negative while evaluating a classifier for multi-class datasets”, a common practice that has been observed in the existing literature. In order to address these hidden challenges for the generalization of a particular classifier, several evaluation metrics are compared for a multi-class dataset with four classes; three of them belong to different neurodegenerative diseases and one to control subjects. Three classifiers, linear discriminant, quadratic discriminant and Parzen are selected to demonstrate the results with examples.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Science & Technology; Technology; Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Computer Science, Theory & Methods; Computer Science; Classifier evaluation; multi-class dataset; pattern recognition; neurodegenerative diseases; multiple classifiers; CLASSIFICATION; ROC
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Computer Science & Mathematics
Publisher: IEEE
Related URLs:
Date Deposited: 27 Nov 2015 09:26
Last Modified: 13 Apr 2022 15:14
DOI or ID number: 10.1109/CISIS.2014.48
URI: https://researchonline.ljmu.ac.uk/id/eprint/2397
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