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Algebraic fusion of multiple classifiers for handwritten digits recognition

Zhao, Huihuang and Liu, Han 2018. Algebraic fusion of multiple classifiers for handwritten digits recognition. Presented at: International Conference on Machine Learning and Cybernetics, Chengdu, China, 15-18 July 2018. 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). pp. 250-255. 10.1109/ICWAPR.2018.8521321

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Abstract

Recognition of handwritten digits is a very popular application of machine learning. In this context, each of the ten digits (0-9) is defined as a class in the setting of machine learning based classification tasks. In general, popular learning methods , such as support vector machine, neural networks and K nearest neighbours, have been used for classifying instances of handwritten digits to one of the ten classes. However, due to the diversity of handwriting styles from different people, it can happen that some handwritten digits (e.g. 4 and 9) are very similar and are thus difficult to distinguish. Also, each single learning algorithm may have its own advantages and disadvantages, which means that a single algorithm would be capable of learning some but not all specific characteristics of handwritten digits. From this point of view, a method for handwritten digits recognition is proposed in the setting of ensemble learning, towards encouraging the diversity among different classifiers trained by different learning algorithms. In particular, the image features of handwritten digits are extracted by using the Convolutional Neural Network architecture. Furthermore, single classifiers trained respectively by K nearest neighbours and random forests are fused as an ensemble one. The experimental results show that the ensemble classifier was able to achieve a recognition accuracy of ≥ 98% using the MNISET data set.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
ISBN: 9781538652183
Funders: BIG Lottery Fund, National Natural Science Foundation of China, Hunan Provincial Natural Science Foundation, Hunan Provincial Education Department
Date of First Compliant Deposit: 3 July 2018
Date of Acceptance: 17 May 2018
Last Modified: 17 Apr 2019 15:00
URI: http://orca-mwe.cf.ac.uk/id/eprint/119816

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