Extending decision tree methods for the analysis of remotely sensed images

A new automated approach for Electrocardiogram (ECG) arrhythmias characterization and classification with the combination of Wavelet transform and Decision tree classification is presented. The approach is based on two key steps. In the first step, the authors adopt the wavelet transform to extract the ECG signals wavelet coefficients as first features and utilize the combination of Principal Component Analysis (PCA) and Fast Independent Component Analysis (FastICA) to transform the first features into uncorrelated and mutually independent new features. In the second step, they utilize some decision tree methods currently in use: C4.5, Improved C4.5, CHAID (Chi - Square Automatic Interaction Detection) and Improved CHAID for the classification of ECG signals, which are taken, from the MIT-BIH database, including normal subjects and subjects affected by arrhythmia. The authors’ results suggest the high reliability and high classification accuracy of C4.5 algorithm with the bootstrap aggregation.

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Network traffic classification — A comparative study of two common decision tree methods: C4.5 and Random forest

2014 2nd International Conference on Electronic Design (ICED) ◽

10.1109/iced.2014.7015800 ◽

2014 ◽

Cited By ~ 6

Author(s):

Alhamza Munther ◽

Alabass Alalousi ◽

Shahrul Nizam ◽

Rozmie R. Othman ◽

Mohammed Anbar

Keyword(s):

Random Forest ◽

Decision Tree ◽

Comparative Study ◽

Network Traffic ◽

Traffic Classification ◽

Network Traffic Classification ◽

Tree Methods ◽

Decision Tree Methods

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Reduced Feature Set for Emotion Based Spoken Utterances of Normal and Special Children Using Multivariate Analysis and Decision Trees

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.2177 ◽

2018 ◽

Vol 8 (4) ◽

pp. 3228-3233

Author(s):

M. A. Siddiqui ◽

S. A. Ali ◽

N. G. Haider

Keyword(s):

Factor Analysis ◽

Multivariate Analysis ◽

Decision Tree ◽

Multivariate Data Analysis ◽

Principal Component ◽

Feature Sets ◽

Tree Methods ◽

Special Children ◽

Children Speech ◽

Decision Tree Methods

The current paper deals with the use of multivariate data analysis and decision tree methods in order to reduce the feature set for the normal and special children speech in four different emotions: anger, happiness, neutral and sadness. Ten features were extracted, by an algorithm implemented in a previous study to classify the speech emotions of normal and special children. In the current study, the best features are selected using multivariate analysis: principal component analysis (PCA), factor analysis and decision tree. Step by step PCA is applied to reduce the feature set according to the variables that are collinear. The obtained reduced feature sets are applicable to both normal and special children samples. Experimental results revealed that PCA yields the feature set comprising pitch, intensity, formant, LPCC and rate of acceleration. Factor analysis provides three feature sets out of which the feature set comprising of Rasta PLP, MFCC, ZCR, and intensity provides the best result. Decision tree yields a feature set comprising energy, pitch and LPCC.

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