A new framework for identifying differentially expressed genes
Pattern Recognition
Classification model for product form design using fuzzy support vector machines
Computers and Industrial Engineering
Liknon Feature Selection for Microarrays
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Two-Step Cross-Entropy Feature Selection for Microarrays—Power Through Complementarity
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: Genomic datasets generated by high-throughput technologies are typically characterized by a moderate number of samples and a large number of measurements per sample. As a consequence, classification models are commonly compared based on resampling techniques. This investigation discusses the conceptual difficulties involved in comparative classification studies. Conclusions derived from such studies are often optimistically biased, because the apparent differences in performance are usually not controlled in a statistically stringent framework taking into account the adopted sampling strategy. We investigate this problem by means of a comparison of various classifiers in the context of multiclass microarray data. Results: Commonly used accuracy-based performance values, with or without confidence intervals, are inadequate for comparing classifiers for small-sample data. We present a statistical methodology that avoids bias in cross-validated model selection in the context of small-sample scenarios. This methodology is valid for both k-fold cross-validation and repeated random sampling. Contact: dp.berrar@ulster.ac.uk