Feature selection for face authentication systems: feature space reductionism and QPSO

AbstractThe greatest problem met when a Brain Computer Interface (BCI) based on electroencephalographic (EEG) signals is to be created is a huge dimensionality of EEG feature space and at the same time very limited number of possible observations. The first is a result of a huge amount of data which can be recorded during the single trial, the latter - the result of individuality of EEG signals, which can significantly differ in different frequency bands determined for different subjects. These two reasons force the brain researches to reduce the huge EEG feature space to only some features, those which allow to build a BCI of a satisfactory accuracy. The paper presents the comparison of two methods of feature selection - blind source separation (BSS) method and method using interpretable features. The comparison was carried out with the data set recorded during EEG session with a subject whose task was to imagine movements of right and left hand.

Download Full-text

Feature space fusion and feature selection for an enhanced robustness of the fingerprint forgery detection for printed artificial sweat

2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) ◽

10.1109/icmew.2015.7169865 ◽

2015 ◽

Author(s):

Mario Hildebrandt

Keyword(s):

Feature Selection ◽

Feature Space ◽

Forgery Detection ◽

Artificial Sweat ◽

Selection For

Download Full-text

Rough set model based feature selection for mixed-type data with feature space decomposition

Expert Systems with Applications ◽

10.1016/j.eswa.2018.03.010 ◽

2018 ◽

Vol 103 ◽

pp. 196-205 ◽

Cited By ~ 5

Author(s):

Kyung-Jun Kim ◽

Chi-Hyuck Jun

Keyword(s):

Feature Selection ◽

Rough Set ◽

Mixed Type ◽

Feature Space ◽

Model Based ◽

Space Decomposition ◽

Selection For ◽

Type Data

Download Full-text

Improve Abstract Data with Feature Selection for Classification Techniques

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.403-408.3699 ◽

2011 ◽

Vol 403-408 ◽

pp. 3699-3703 ◽

Cited By ~ 1

Author(s):

Vatinee Nuipian ◽

Phayung Meesad ◽

Pudsadee Boonrawd

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

Feature Space ◽

Classification Performance ◽

Support Vector ◽

Statistical Pattern ◽

Abstract Data ◽

Selection For ◽

Frequency Vectors

A universal problem with text classification has a problem due to the high dimensionality of feature space, e.g. word frequency vectors. To overcome this problem, this paper proposed a feature selection which focuses on statistical pattern based on SVM Attribute. Experiments have shown that the determination of word importance may increase the speed of the classification algorithm and save their resource used significantly. The proposed method was studied by comparing classification performance among Decision Tree, Naïve Bayes, and Support Vector Machine. The results showed that Support Vector Machine was found to be the best algorithm with F-measure 93.6%. It is found that the feature selection can reduce dimensionality of data significantly.

Download Full-text