ArticlePloS one2014
A systematic comparison of supervised classifiers.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
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- Accurate staging of chick embryonic tissues via deep learning of salient features.Development (Cambridge, England) · 2023Article
- Evaluation of a decided sample size in machine learning applications.BMC bioinformatics · 2023Article
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- Evaluation of Arabian Vascular Plant Barcodes (rbcL and matK): Precision of Unsupervised and Supervised Learning Methods towards Accurate Identification.Plants (Basel, Switzerland) · 2021Article
- An Approach to Growth Delimitation of Straight Line Segment Classifiers Based on a Minimum Bounding Box.Entropy (Basel, Switzerland) · 2021Article
- Challenges and Opportunities in the Statistical Analysis of Multiplex Immunofluorescence Data.Cancers · 2021Review
- Domain-specific introduction to machine learning terminology, pitfalls and opportunities in CRISPR-based gene editing.Briefings in bioinformatics · 2021Review
- Human Sentiment and Activity Recognition in Disaster Situations Using Social Media Images Based on Deep Learning.Sensors (Basel, Switzerland) · 2020Article
- Machine Learning-Based Activity Pattern Classification Using Personal PMInternational journal of environmental research and public health · 2020Article
- Motion synchronisation patterns of the carotid atheromatous plaque from B-mode ultrasound.Scientific reports · 2020Observational
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pattern recognition has been employed in a myriad of industrial, commercial and academic applications. Many techniques have been devised to tackle such a diversity of applications. Despite the long tradition of pattern recognition research, there is no technique that yields the best classification in all scenarios. Therefore, as many techniques as possible should be considered in high accuracy applications. Typical related works either focus on the performance of a given algorithm or compare various classification methods. In many occasions, however, researchers who are not experts in the field of machine learning have to deal with practical classification tasks without an in-depth knowledge about the underlying parameters. Actually, the adequate choice of classifiers and parameters in such practical circumstances constitutes a long-standing problem and is one of the subjects of the current paper. We carried out a performance study of nine well-known classifiers implemented in the Weka framework and compared the influence of the parameter configurations on the accuracy. The default configuration of parameters in Weka was found to provide near optimal performance for most cases, not including methods such as the support vector machine (SVM). In addition, the k-nearest neighbor method frequently allowed the best accuracy. In certain conditions, it was possible to improve the quality of SVM by more than 20% with respect to their default parameter configuration.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.