Applying Weights in the Functioning of the Dynamic Classifier Selection Method

2006 ◽  
Author(s):  
Diogo Fagundes ◽  
Anne Canuto
Keyword(s):  
Selection Method ◽  
Classifier Selection ◽  
Dynamic Classifier Selection

A novel multi-stage ensemble model with fuzzy clustering and optimized classifier composition for corporate bankruptcy prediction

2020 ◽  
pp. 1-17
Author(s):  
Dongqi Yang ◽  
Wenyu Zhang ◽  
Xin Wu ◽  
Jose H. Ablanedo-Rosas ◽  
Lingxiao Yang ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Rapid Development ◽  
Optimization Method ◽  
Selection Method ◽  
Bankruptcy Prediction ◽  
Ensemble Model ◽  
Composition Method ◽  
Classifier Selection ◽  
Multi Stage ◽  
Proposed Model

With the rapid development of commercial credit mechanisms, credit funds have become fundamental in promoting the development of manufacturing corporations. However, large-scale, imbalanced credit application information poses a challenge to accurate bankruptcy predictions. A novel multi-stage ensemble model with fuzzy clustering and optimized classifier composition is proposed herein by combining the fuzzy clustering-based classifier selection method, the random subspace (RS)-based classifier composition method, and the genetic algorithm (GA)-based classifier compositional optimization method to achieve accuracy in predicting bankruptcy among corporates. To overcome the inherent inflexibility of traditional hard clustering methods, a new fuzzy clustering-based classifier selection method is proposed based on the mini-batch k-means algorithm to obtain the best performing base classifiers for generating classifier compositions. The RS-based classifier composition method was applied to enhance the robustness of candidate classifier compositions by randomly selecting several subspaces in the original feature space. The GA-based classifier compositional optimization method was applied to optimize the parameters of the promising classifier composition through the iterative mechanism of the GA. Finally, six datasets collected from the real world were tested with four evaluation indicators to assess the performance of the proposed model. The experimental results showed that the proposed model outperformed the benchmark models with higher predictive accuracy and efficiency.

Semi-Supervised Hyperspectral Band Selection Based on Dynamic Classifier Selection

2019 ◽  
Vol 12 (4) ◽  
pp. 1289-1298 ◽  
Author(s):  
Xianghai Cao ◽  
Cuicui Wei ◽  
Yiming Ge ◽  
Jie Feng ◽  
Jing Zhao ◽  
...  
Keyword(s):  
Band Selection ◽  
Classifier Selection ◽  
Dynamic Classifier Selection

scholarly journals A Robust Dynamic Classifier Selection Approach for Hyperspectral Images with Imprecise Label Information

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5262
Author(s):  
Meizhu Li ◽  
Shaoguang Huang ◽  
Jasper De Bock ◽  
Gert de Cooman ◽  
Aleksandra Pižurica
Keyword(s):  
Hyperspectral Image ◽  
Probability Distributions ◽  
Imprecise Probabilities ◽  
Data Sets ◽  
Classifier Selection ◽  
Training Samples ◽  
Proposed Model ◽  
Label Information ◽  
Dynamic Classifier Selection ◽  
The Individual

Supervised hyperspectral image (HSI) classification relies on accurate label information. However, it is not always possible to collect perfectly accurate labels for training samples. This motivates the development of classifiers that are sufficiently robust to some reasonable amounts of errors in data labels. Despite the growing importance of this aspect, it has not been sufficiently studied in the literature yet. In this paper, we analyze the effect of erroneous sample labels on probability distributions of the principal components of HSIs, and provide in this way a statistical analysis of the resulting uncertainty in classifiers. Building on the theory of imprecise probabilities, we develop a novel robust dynamic classifier selection (R-DCS) model for data classification with erroneous labels. Particularly, spectral and spatial features are extracted from HSIs to construct two individual classifiers for the dynamic selection, respectively. The proposed R-DCS model is based on the robustness of the classifiers’ predictions: the extent to which a classifier can be altered without changing its prediction. We provide three possible selection strategies for the proposed model with different computational complexities and apply them on three benchmark data sets. Experimental results demonstrate that the proposed model outperforms the individual classifiers it selects from and is more robust to errors in labels compared to widely adopted approaches.

Using weighted dynamic classifier selection methods in ensembles with different levels of diversity

2006 ◽  
Vol 3 (3) ◽  
pp. 147-158 ◽  
Author(s):  
Anne M.P. Canuto ◽  
Diogo Fagundes ◽  
Marjory C.C. Abreu ◽  
João C. Xavier Junior
Keyword(s):  
Selection Methods ◽  
Classifier Selection ◽  
Dynamic Classifier Selection ◽  
Different Levels

scholarly journals From dynamic classifier selection to dynamic ensemble selection

2008 ◽  
Vol 41 (5) ◽  
pp. 1718-1731 ◽  
Author(s):  
Albert H.R. Ko ◽  
Robert Sabourin ◽  
Alceu Souza Britto, Jr.
Keyword(s):  
Classifier Selection ◽  
Ensemble Selection ◽  
Dynamic Classifier Selection ◽  
Dynamic Ensemble Selection
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