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A systematic comparison of supervised classifiers.
Diego Raphael Amancio1, Cesar Henrique Comin2, Dalcimar Casanova2
1Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, São Paulo, Brazil.
Plos One
|April 26, 2014
Summary
Choosing the right machine learning classifier and parameters is crucial for accurate pattern recognition. Default settings often work well, but tuning parameters, especially for Support Vector Machines (SVM), can significantly boost accuracy.
Area of Science:
- Computer Science
- Machine Learning
- Pattern Recognition
Background:
- Pattern recognition is widely used across industries and academia.
- No single pattern recognition technique excels in all applications.
- Selecting appropriate classifiers and parameters is challenging for non-experts.
Purpose of the Study:
- To evaluate the performance of nine common classifiers in the Weka framework.
- To investigate the impact of parameter configurations on classification accuracy.
- To provide guidance for practical classification tasks.
Main Methods:
- Performance study of nine well-known classifiers.
- Comparison of classification accuracy across different parameter settings.
- Utilized the Weka machine learning framework.
Main Results:
- Default Weka parameters offer near-optimal performance for most classifiers.
- Support Vector Machines (SVM) performance can be significantly improved by parameter tuning.
- K-nearest neighbor (KNN) frequently achieved the highest accuracy.
Conclusions:
- Parameter optimization is essential for maximizing classifier performance, particularly for SVM.
- Default settings are a good starting point, but not always sufficient.
- KNN is a strong contender for high-accuracy pattern recognition tasks.
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