Adapted ensemble classification algorithm based on multiple classifier system and feature selection for classifying multi-class imbalanced data

2016 ◽  
Vol 94 ◽  
pp. 88-104 ◽  
Author(s):  
Li Yijing ◽  
Guo Haixiang ◽  
Liu Xiao ◽  
Li Yanan ◽  
Li Jinling
Keyword(s):  
Feature Selection ◽  
Imbalanced Data ◽  
Classification Algorithm ◽  
Ensemble Classification ◽  
Multiple Classifier System ◽  
Classifier System ◽  
Multiple Classifier ◽  
Selection For

scholarly journals Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection

2017 ◽  
Vol 231 (11) ◽  
pp. 1048-1063 ◽  
Author(s):  
Omneya Attallah ◽  
Alan Karthikesalingam ◽  
Peter JE Holt ◽  
Matthew M Thompson ◽  
Rob Sayers ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feature Selection ◽  
Survival Data ◽  
Majority Voting ◽  
Selection Methods ◽  
Multiple Classifier System ◽  
Machine Learning Classifiers ◽  
Classifier System ◽  
Multiple Classifier

Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox’s proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox’s model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients’ future follow-up plan.

Microarray Data Classification Based on Evolutionary Multiple Classifier System

2011 ◽  
Vol 130-134 ◽  
pp. 2077-2080
Author(s):  
Zheng Gang Gu ◽  
Kun Hong Liu
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Selection Process ◽  
Data Classification ◽  
Research Area ◽  
Ensemble Method ◽  
Multiple Classifier System ◽  
Classifier System ◽  
Classifier Selection ◽  
Multiple Classifier

Designing an evolutionary multiple classifier system (MCS) is a relatively new research area. In this paper, we propose a genetic algorithm (GA) based MCS for microarray data classification. We construct a feature poll with different feature selection methods first, and then a multi-objective GA is applied to implement ensemble feature selection process so as to generate a set of classifiers. When this GA stops, a set of base classifiers are generated. Here we use all the nondominated individuals in last generation to build an ensemble system and test the proposed ensemble method and the method that apply a classifier selection process to select proper classifiers from all the individuals in last generation. The experimental results show the proposed ensemble method is roubust and can lead to promising results.

scholarly journals A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Chenchen Huang ◽  
Wei Gong ◽  
Wenlong Fu ◽  
Dongyu Feng
Keyword(s):  
Feature Extraction ◽  
Emotion Recognition ◽  
Recognition Rate ◽  
Original Method ◽  
Speech Emotion Recognition ◽  
High Dimensional ◽  
Svm Classifier ◽  
Multiple Classifier System ◽  
Classifier System ◽  
Multiple Classifier

Feature extraction is a very important part in speech emotion recognition, and in allusion to feature extraction in speech emotion recognition problems, this paper proposed a new method of feature extraction, using DBNs in DNN to extract emotional features in speech signal automatically. By training a 5 layers depth DBNs, to extract speech emotion feature and incorporate multiple consecutive frames to form a high dimensional feature. The features after training in DBNs were the input of nonlinear SVM classifier, and finally speech emotion recognition multiple classifier system was achieved. The speech emotion recognition rate of the system reached 86.5%, which was 7% higher than the original method.

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