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Sakarya University Journal of Science
SAUJS
e-ISSN 2147-835X | Period Bimonthly | Founded: 1997 | Publisher Sakarya University |
Title: Vote-Based: Ensemble Approach
Authors: Abdul Ahad ABRO
Recieved: 2021-03-23 18:56:37
Accepted: 2021-05-31 09:24:51
Article Type: Research Article
Volume: 25
Issue: 3
Month: June
Year: 2021
Pages: 858-866
How to cite
Abdul Ahad ABRO; (2021), Vote-Based: Ensemble Approach. Sakarya University
Journal of Science, 25(3), 858-866, DOI:
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Vote-Based: Ensemble Approach
Abdul Ahad ABRO* 1
Abstract
Vote-based is one of the ensembles learning methods in which the individual classifier is
situated on numerous weighted categories of the training datasets. In designing a method,
training, validation and test sets are applied in terms of an ensemble approach to developing an
efficient and robust binary classification model. Similarly, ensemble learning is the most
prominent and broad research area of Machine Learning (ML) and image recognition, which
assists in enhancing the capability of performance. In most cases, the ensemble learning
algorithm yields better performance than ML algorithms. In this regard, numerous approaches
had been studied significantly and used to accomplish better yields from the existing
literature; however, the outcomes of these methods are inadequate. Unlike existing methods,
the proposed technique aggregates an ensemble classifier, known as vote-based, to employ and
integrate the advantage of ML classifiers, which are Naive Bayes (NB), Artificial Neural
Network (ANN) and Logistic Model Tree (LMT). This paper proposes an ensemble framework
that aims to evaluate datasets from the UCI ML repository by adopting performance analysis.
The experimental consequences reveal that the intended approach outperforms than the
conventional approaches. Furthermore, the experimental outputs indicate that the suggested
method provides more accurate results according to the base learner approaches in terms of
accuracy rates, an area under the curve (AUC), recall, precision, and F-measure values. This
method can be used for binary classification, image recognition and machine learning problems.
Keywords: Machine Learning, Artificial Neural Network (ANN), Ensemble learning, Data
Mining, Classification.
1. INTRODUCTION
Machine Learning and Ensemble Learning
multiple approaches intend to merge specific
decisions by weighted or unweighted vote-based
to classify new events as an active research area.
These systems are mainly aimed towards
achieving efficient results in classification rather
*Corresponding author: [email protected]
1 Department of Computer Engineering, Ege University, Turkey.