Floating search methods in feature selection
Pattern Recognition Letters
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Fixed-Point Algorithm and Maximum Likelihood Estimation forIndependent Component Analysis
Neural Processing Letters
The Journal of Machine Learning Research
Neural Networks - 2005 Special issue: IJCNN 2005
Random subspace for an improved BioHashing for face authentication
Pattern Recognition Letters
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
An automated palmprint recognition system
Image and Vision Computing
Wavelet selection for disease classification by DNA microarray data
Expert Systems with Applications: An International Journal
Discriminant orthogonal rank-one tensor projections for face recognition
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
A novel method for micro-array data classification based on orthogonal linear discriminant analysis (ODA), sequential forward floating selection (SFFS) and support vector machine (SVM) is here proposed. In this paper, in order to avoid the constraint that the dimension of the ODA subspace is bounded by the number of classes, to increase the dimension of the subspace and to improve the accuracy, we combine the ''original'' features to obtain new features. We combine the features in groups of K, each new feature f is obtained by the projection that maps the K-dimensional feature space to a single dimension. A feature selection algorithm is applied to select the most relevant features. Since the new features space has only few hundreds of features an exhaustive wrapper feature selection approach is used to select the set of relevant features. Finally a radial basis function SVM is trained using these features. The obtained results are very encouraging, they improve the average predictive accuracy obtained using standard feature transform techniques. Particularly interesting are the results on a breast cancer dataset, to the best of our knowledge the proposed method is the first method that, using the genes information, permits to determine with high accuracy if a person might benefit from adjuvant chemotherapy.