Classification of pyrolysis mass spectra by fuzzy multivariate rule induction-comparison with regression, K-nearest neighbour, neural and decision-tree methods

1997 ◽  
Vol 348 (1-3) ◽  
pp. 389-407 ◽  
Author(s):  
B.K. Alsberg ◽  
R. Goodacre ◽  
J.J. Rowland ◽  
D.B. Kell
Keyword(s):  
Decision Tree ◽  
Mass Spectra ◽  
Rule Induction ◽  
Nearest Neighbour ◽  
Tree Methods ◽  
Decision Tree Methods

SETTING UP A PROBABILISTIC NEURAL NETWORK FOR CLASSIFICATION OF HIGHWAY VEHICLES

2005 ◽  
Vol 05 (04) ◽  
pp. 411-423 ◽  
Author(s):  
MAJURA F. SELEKWA ◽  
VALERIAN KWIGIZILE ◽  
RENATUS N. MUSSA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Tree ◽  
Probabilistic Neural Network ◽  
Classification Problem ◽  
Misclassification Rate ◽  
Tree Methods ◽  
Decision Tree Methods

Many neural network methods used for efficient classification of populations work only when the population is globally separable. In situ classification of highway vehicles is one of the problems with globally nonseparable populations. This paper presents a systematic procedure for setting up a probabilistic neural network that can classify the globally nonseparable population of highway vehicles. The method is based on a simple concept that any set of classifiable data can be broken down to subclasses of locally separable data. Hence, if these locally separable data can be identified, then the classification problem can be carried out in two hierarchical steps; step one classifies the data according to the local subclasses, and step two classifies the local subclasses into the global classes. The proposed approach was tested on the problem of classifying highway vehicles according to the US Federal Highway Administration standard, which is normally handled by decision tree methods that use vehicle axle information and a set of IF-THEN rules. By using a sample of 3326 vehicles, the proposed method showed improved classification results with an overall misclassification rate of only 2.9% compared to 9.7% of the decision tree methods. A similar setup can be used with different neural networks such as recurrent neural networks, but they were not tested in this study especially since the focus was for in situ applications where a high learning rate is desired.

Comparisons of decision tree methods using water data

2016 ◽  
Vol 46 (4) ◽  
pp. 2924-2934 ◽  
Author(s):  
Muhammad Azam ◽  
Muhammad Aslam ◽  
Khushnoor Khan ◽  
Anwar Mughal ◽  
Awais Inayat
Keyword(s):  
Decision Tree ◽  
Tree Methods ◽  
Decision Tree Methods

An Approach for ECG Characterization and Classification Using the Combination of Wavelet Transform and Decision Tree Methods

2012 ◽  
Vol 1 (3) ◽  
pp. 72-81
Author(s):  
Faiza Charfi ◽  
Ali Kraiem
Keyword(s):  
Wavelet Transform ◽  
Decision Tree ◽  
High Reliability ◽  
Principal Component ◽  
Component Analysis ◽  
Ecg Signals ◽  
Interaction Detection ◽  
Fast Independent Component Analysis ◽  
Tree Methods ◽  
Decision Tree Methods

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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