Local adaptive learning for semi-supervised feature selection with group sparsity

Classification of epileptic seizure using feature selection based on fuzzy membership from EEG signal

BACKGROUND:

Feature selection is a technology that improves the performance result by eliminating overlapping or unrelated features.

OBJECTIVE:

To improve the performance result, this study proposes a new feature selection that uses the distance between the centers.

METHODS:

This study uses the distance between the centers of gravity (DBCG) of the bounded sum of the weighted fuzzy memberships (BSWFMs) supported by a neural network with weighted fuzzy membership (NEWFM).

RESULTS:

Using distance-based feature selection, 22 minimum features with a high performance result are selected, with the shortest DBCG of BSWFMs removed individually from the initial 24 features. The NEWFM used 22 minimum features as inputs to obtain a sensitivity, accuracy, and specificity of 99.3%, 99.5%, and 99.7%, respectively.

CONCLUSIONS:

In this study, only the mean DBCG is used to select the features; in the future, however, it will be necessary to incorporate statistical methods such as the standard deviation, maximum, and normal distribution.