Floating search methods in feature selection
Pattern Recognition Letters
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature Selection via Discretization
IEEE Transactions on Knowledge and Data Engineering
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
An efficient classifier to diagnose of schizophrenia based on the EEG signals
Expert Systems with Applications: An International Journal
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Artificial Intelligence in Medicine
Ensemble-based discriminant learning with boosting for face recognition
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
There is a growing interest to employ synchronization methods to reveal natural connections among the brain lobes by measuring co-activations among EEG channels. Regarding high number of EEG channels, lots of synchronization indexes are determined between two by two channels leading to construct a high dimensional feature vector for each time frame. The objective of this paper is to propose an effective feature selection method to find discriminative synchronization indexes in order to classify patients with schizophrenia from those with bipolar mood disorder (BMD). The state-of-art synchronization methods from various domains such as phase-locking value (PLV), robust synchronization (RS), and synchronization likelihood (SL), were implemented to provide a rich feature set in order to classify the two groups. To increase the classification accuracy, a capable feature selection scheme is proposed entitled greedy overall relevancy (GOR) to select discriminative synchronization indexes. The elicited synchronization vectors of 53 subjects imposed to support vector machine (SVM) classifier and the classification result with and without applying GOR, provided 92.45% and 88.68% accuracy, respectively. Across-group variance (AGV) is chosen as a rival method to GOR; the selected features by AGV entered to the classifier resulting in 91.34% accuracy. Using pair T-test exhibits the significant superiority of GOR to AGV such that P-value determined less than 0.05. To the best of authors' knowledge, this is the first attempt to utilize the selected synchronization indexes as informative features applying to a classifier for diagnosing the psychiatric patients.