Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine Learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Meta Analysis of Classification Algorithms for Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Estimating the Predictive Accuracy of a Classifier
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Characterization of Classification Algorithms
EPIA '95 Proceedings of the 7th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Zoomed Ranking: Selection of Classification Algorithms Based on Relevant Performance Information
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Discovering Task Neighbourhoods Through Landmark Learning Performances
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Improved Dataset Characterisation for Meta-learning
DS '02 Proceedings of the 5th International Conference on Discovery Science
An introduction to variable and feature selection
The Journal of Machine Learning Research
A tutorial on support vector regression
Statistics and Computing
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
The lack of a priori distinctions between learning algorithms
Neural Computation
Information-Theoretic Measures for Meta-learning
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Bioinformatics
On learning algorithm selection for classification
Applied Soft Computing
Filter methods for feature selection: a comparative study
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Meta-data: characterization of input features for meta-learning
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Fast orthogonal forward selection algorithm for feature subset selection
IEEE Transactions on Neural Networks
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Most of the widely used pattern classification algorithms, such as Support Vector Machines (SVM), are sensitive to the presence of irrelevant or redundant features in the training data. Automatic feature selection algorithms aim at selecting a subset of features present in a given dataset so that the achieved accuracy of the following classifier can be maximized. Feature selection algorithms are generally categorized into two broad categories: algorithms that do not take the following classifier into account (the filter approaches), and algorithms that evaluate the following classifier for each considered feature subset (the wrapper approaches). Filter approaches are typically faster, but wrapper approaches deliver a higher performance. In this paper, we present the algorithm - Predictive Forward Selection - based on the widely used wrapper approach forward selection. Using ideas from meta-learning, the number of required evaluations of the target classifier is reduced by using experience knowledge gained during past feature selection runs on other datasets. We have evaluated our approach on 59 real-world datasets with a focus on SVM as the target classifier. We present comparisons with state-of-the-art wrapper and filter approaches as well as one embedded method for SVM according to accuracy and run-time. The results show that the presented method reaches the accuracy of traditional wrapper approaches requiring significantly less evaluations of the target algorithm. Moreover, our method achieves statistically significant better results than the filter approaches as well as the embedded method.