Fusion of Ensemble Classifiers for Handwritten Recognition
Fusion of Ensemble Classifiers for Handwritten Recognition |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-5 |
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| Year of Publication : 2024 | ||
| Author : M. Govindarajan |
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| DOI : 10.14445/22315381/IJETT-V72I5P110 | ||
How to Cite?
M. Govindarajan, "Fusion of Ensemble Classifiers for Handwritten Recognition," International Journal of Engineering Trends and Technology, vol. 72, no. 5, pp. 99-104, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I5P110
Abstract
In the past few years, ensemble techniques were broadly utilized for the task of handwritten recognition rather than single classifiers, which has proved that misclassifications made by classifiers utilizing different feature sets may not certainly overlap. Consequently, integrating classifiers increases the base classifiers' accuracy. In this research work, novel ensemble approaches are presented that are composed of arcing of heterogeneous ensembles and bagged homogeneous classifier ensembles. Then, using accuracy, the classifier performs the classification performances that are assessed using precision. Here, using base classifiers, a classifier ensemble is built, like RBF and SVM. The advantages and feasibleness of the presented approaches are proved using the prevailing handwritten recognition dataset. The major novelty of the study relies on three sub processes: pre-processing, categorization and integrating. An extensive array of analogous investigations is completed for a typical database of handwritten recognition. Meanwhile, a comparison study with prior research on the database of handwritten recognition is also revealed. The investigational results reveal that this suggested group approach is reasonable.
Keywords
Arcing, Bagging, Ensemble, Support Vector Machine, Radial Basis Function.
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