Elements of statistical computing
Elements of statistical computing
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
A comparative assessment of classification methods
Decision Support Systems
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
Feature selection, L1 vs. L2 regularization, and rotational invariance
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A Fast Dual Algorithm for Kernel Logistic Regression
Machine Learning
The use of receiver operating characteristic curves in biomedical informatics
Journal of Biomedical Informatics - Special issue: Clinical machine learning
Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Algorithms for Sparse Linear Classifiers in the Massive Data Setting
The Journal of Machine Learning Research
EfficientL1regularized logistic regression
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Computational Statistics & Data Analysis
Multivariate selection of genetic markers in diagnostic classification
Artificial Intelligence in Medicine
Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System
Journal of Medical Systems
A novel classification learning framework based on estimation of distribution algorithms
International Journal of Computing Science and Mathematics
Hi-index | 12.05 |
Regularized logistic regression is a useful classification method for problems with few samples and a huge number of variables. This regression needs to determine the regularization term, which amounts to searching for the optimal penalty parameter and the norm of the regression coefficient vector. This paper presents a new regularized logistic regression method based on the evolution of the regression coefficients using estimation of distribution algorithms. The main novelty is that it avoids the determination of the regularization term. The chosen simulation method of new coefficients at each step of the evolutionary process guarantees their shrinkage as an intrinsic regularization. Experimental results comparing the behavior of the proposed method with Lasso and ridge logistic regression in three cancer classification problems with microarray data are shown.