Data selection for support vector machine classifiers
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Variable selection using svm based criteria
The Journal of Machine Learning Research
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Feature selection, L1 vs. L2 regularization, and rotational invariance
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Combined SVM-Based Feature Selection and Classification
Machine Learning
Neural Computation
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Direct convex relaxations of sparse SVM
Proceedings of the 24th international conference on Machine learning
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Supervised feature selection via dependence estimation
Proceedings of the 24th international conference on Machine learning
Discriminative semi-supervised feature selection via manifold regularization
IEEE Transactions on Neural Networks
Novel method for feature-set ranking applied to physical activity recognition
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Online feature selection for mining big data
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Feature extraction in protein sequences classification: a new stability measure
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Feature selection for link prediction
Proceedings of the 5th Ph.D. workshop on Information and knowledge
Feature selection for high-dimensional imbalanced data
Neurocomputing
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We consider the problem of selecting a subset of m most informative features where m is the number of required features. This feature selection problem is essentially a combinatorial optimization problem, and is usually solved by an approximation. Conventional feature selection methods address the computational challenge in two steps: (a) ranking all the features by certain scores that are usually computed independently from the number of specified features m, and (b) selecting the top m ranked features. One major shortcoming of these approaches is that if a feature f is chosen when the number of specified features is m, it will always be chosen when the number of specified features is larger than m. We refer to this property as the "monotonic" property of feature selection. In this work, we argue that it is important to develop efficient algorithms for non-monotonic feature selection. To this end, we develop an algorithm for non-monotonic feature selection that approximates the related combinatorial optimization problem by a Multiple Kernel Learning (MKL) problem. We also present a strategy that derives a discrete solution from the approximate solution of MKL, and show the performance guarantee for the derived discrete solution when compared to the global optimal solution for the related combinatorial optimization problem. An empirical study with a number of benchmark data sets indicates the promising performance of the proposed framework compared with several state-of-the-art approaches for feature selection.