Solving the Problem of the K Parameter in the KNN Classifier Using an Ensemble Learning Approach

This paper presents a new solution for choosing the K parameter in the k-nearest neighbor (KNN) algorithm; the solution depending on the idea of ensemble learning, in which a weak KNN classifier is used each time with a different K, starting from one to the square root of the size of the training se...

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Bibliographic Details
Published in:International journal of computer science and information security 2014-08, Vol.12 (8), p.33-33
Main Authors: Hassanat, Ahmad Basheer, Abbadi, Mohammad Ali, Altarawneh, Ghada Awad, Alhasanat, Ahmad Ali
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classification
Classifiers
Computer information security
Learning
Mathematical models
Methods
Pattern recognition
Roots
Sum rules
Training
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Summary:This paper presents a new solution for choosing the K parameter in the k-nearest neighbor (KNN) algorithm; the solution depending on the idea of ensemble learning, in which a weak KNN classifier is used each time with a different K, starting from one to the square root of the size of the training set. The results of the weak classifiers are combined using the weighted sum rule. The proposed solution was tested and compared to other solutions using a group of experiments in real life problems. The experimental results show that the proposed classifier outperforms the traditional KNN classifier -- that uses a different number of neighbors, competitive with other classifiers, and is a promising classifier with strong potential for a wide range of applications.
ISSN:1947-5500