Using multi decision tree technique to improving decision tree classifier

Maazouzi, Faiz; Bahi, Halima

doi:10.1504/ijbidm.2012.051712

Cited by 6 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DT can change the complicated decision-making problems into simple processes with minimum computation time [ 49 ]. The advantages of the algorithm include that they are relatively easy to interpret and have good classification performance on many datasets.…”

Section: Methodsmentioning

confidence: 99%

EEG-Based Emotion Recognition Using an Improved Weighted Horizontal Visibility Graph

Kong

Shao

et al. 2021

Sensors

View full text Add to dashboard Cite

Emotion recognition, as a challenging and active research area, has received considerable awareness in recent years. In this study, an attempt was made to extract complex network features from electroencephalogram (EEG) signals for emotion recognition. We proposed a novel method of constructing forward weighted horizontal visibility graphs (FWHVG) and backward weighted horizontal visibility graphs (BWHVG) based on angle measurement. The two types of complex networks were used to extract network features. Then, the two feature matrices were fused into a single feature matrix to classify EEG signals. The average emotion recognition accuracies based on complex network features of proposed method in the valence and arousal dimension were 97.53% and 97.75%. The proposed method achieved classification accuracies of 98.12% and 98.06% for valence and arousal when combined with time-domain features.

show abstract

Section: Methodsmentioning

confidence: 99%