A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Information Sciences: an International Journal - Special issue: Soft computing data mining
Information Sciences: an International Journal
A Direct Method of Nonparametric Measurement Selection
IEEE Transactions on Computers
On fuzzy approximation operators in attribute reduction with fuzzy rough sets
Information Sciences: an International Journal
Attribute reduction in decision-theoretic rough set models
Information Sciences: an International Journal
Neighborhood rough set based heterogeneous feature subset selection
Information Sciences: an International Journal
Trust Region Newton Method for Logistic Regression
The Journal of Machine Learning Research
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
A wrapper method for feature selection using Support Vector Machines
Information Sciences: an International Journal
Feature selection for multi-label naive Bayes classification
Information Sciences: an International Journal
Feature selection via Boolean independent component analysis
Information Sciences: an International Journal
Attribute selection with fuzzy decision reducts
Information Sciences: an International Journal
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The classification of animal feed ingredients has become a challenging computational task since the food crisis that arose in the European Union after the outbreak of bovine spongiform encephalopathy (BSE). The most interesting alternative to replace visual observation under classical microscopy is based on the use of near infrared reflectance microscopy (NIRM). This technique collects spectral information from a set of microscopic particles of animal feeds. These spectra can be classified using maximum margin classifiers with good results. However, it is difficult to interpret the models in terms of the contribution of features. To gain insight into the interpretability of such classifications, we propose a method that learns accurate classifiers defined on a small set of narrow intervals of wavelengths. The proposed method is a greedy bipartite procedure that may be successfully compared with other state-of-the-art feature selectors and can be scaled up efficiently to deal with other classification tasks of higher dimensionality.