Liknon Feature Selection for Microarrays
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Data Complexity Analysis: Linkage between Context and Solution in Classification
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
On dataset complexity for case base maintenance
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
Hi-index | 0.00 |
We investigated the geometrical complexity of several high-dimensional, small sample classification problems and its changes due to two popular feature selection procedures, forward feature selection (FFS) and Linear Programming Support Vector Machine (LPSVM). We found that both procedures are able to transform the problems to spaces of very low dimensionality where class separability is improved over that in the original space. The study shows that geometrical complexities have good potentials for comparing different feature selection methods in aspects relevant to classification accuracy, yet independent of particular classifier choices.