Automatic Classifier Selection Based on Classification Complexity

2018 ◽  
pp. 292-303 ◽  
Author(s):  
Liping Deng ◽  
Wen-Sheng Chen ◽  
Binbin Pan
Keyword(s):  
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.

Classifier selection for majority voting

2005 ◽  
Vol 6 (1) ◽  
pp. 63-81 ◽  
Author(s):  
Dymitr Ruta ◽  
Bogdan Gabrys
Keyword(s):  
Majority Voting ◽  
Classifier Selection ◽  
Selection For

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

Rank Aggregation for Pattern Classifier Selection in Remote Sensing Images

2014 ◽  
Vol 7 (4) ◽  
pp. 1103-1115 ◽  
Author(s):  
Fabio A. Faria ◽  
Daniel C. G. Pedronette ◽  
Jefersson A. dos Santos ◽  
Anderson Rocha ◽  
Ricardo da S. Torres
Keyword(s):  
Remote Sensing ◽  
Rank Aggregation ◽  
Remote Sensing Images ◽  
Classifier Selection ◽  
Pattern Classifier

Classifier Selection Approaches for Multi-label Problems

2011 ◽  
pp. 167-176 ◽  
Author(s):  
Ignazio Pillai ◽  
Giorgio Fumera ◽  
Fabio Roli
Keyword(s):  
Classifier Selection

A Knowledge-Based Web Information System for the Fusion of Distributed Classifers

2004 ◽  
pp. 268-304 ◽  
Author(s):  
Grigorios Tsoumakas ◽  
Nick Bassiliades ◽  
Ioannis Vlahavas
Keyword(s):  
Information System ◽  
Distributed Databases ◽  
Semantic Heterogeneity ◽  
Knowledge Based ◽  
Classifier Selection ◽  
Web Information System ◽  
Web Information ◽  
Geographically Distributed ◽  
Fusion Of Classifiers ◽  
The Web

This chapter presents the design and development of WebDisC, a knowledge-based web information system for the fusion of classifiers induced at geographically distributed databases. The main features of our system are: (i) a declarative rule language for classifier selection that allows the combination of syntactically heterogeneous distributed classifiers; (ii) a variety of standard methods for fusing the output of distributed classifiers; (iii) a new approach for clustering classifiers in order to deal with the semantic heterogeneity of distributed classifiers, detect their interesting similarities and differences, and enhance their fusion; and (iv) an architecture based on the Web services paradigm that utilizes the open and scalable standards of XML and SOAP.

A Steganalytic Scheme Based on Classifier Selection Using Joint Image Characteristics

2020 ◽  
pp. 362-376
Author(s):  
Jie Zhu ◽  
Qingxiao Guan ◽  
Xianfeng Zhao ◽  
Yun Cao ◽  
Gong Chen
Keyword(s):  
Image Size ◽  
Size Quantization ◽  
Classification Techniques ◽  
Appreciable Difference ◽  
Classifier Selection ◽  
Image Characteristics ◽  
Improve Accuracy ◽  
Different Characteristics ◽  
Selection Of

Steganalysis relies on steganalytic features and classification techniques. Because of the complexity and different characteristics of cover images, to make steganalysis more applicable toward detecting stego images in real applications, we need to train different classifiers so as to match different images according to their characteristics. Selection of classifiers according to characteristics of images is the key point to improve accuracy of steganalysis. In our work, we study the methods of classifier selection based on characteristics of images including image size, quantization factor, or matrix. Besides, we also discuss other characteristics, such as texture, cover source, which makes an appreciable difference to steganalysis.

Insect Recognition Using Sparse Coding and Decision Fusion

2015 ◽  
pp. 124-145
Author(s):  
Hanqing Lu ◽  
Xinwen Hou ◽  
Cheng-Lin Liu ◽  
Xiaolin Chen
Keyword(s):  
Sparse Coding ◽  
Degrees Of Freedom ◽  
Decision Fusion ◽  
Classifier Fusion ◽  
Hard Problem ◽  
Classifier Selection ◽  
Image Patches ◽  
The Difference ◽  
Fusion Scheme ◽  
Heterogeneous Class

Insect recognition is a hard problem because the difference of appearance between insects is so small that only some entomologist experts can distinguish them. Besides that, insects are often composed of several parts (multiple views) which generate more degrees of freedom. This chapter proposes several discriminative coding approaches and one decision fusion scheme of heterogeneous class sets for insect recognition. The three discriminative coding methods use class specific concatenated vectors instead of traditional global coding vectors for insect image patches. The decision fusion scheme uses an allocation matrix for classifier selection and a weight matrix for classifier fusion, which is suitable for combining classifiers of heterogeneous class sets in multi-view insect image recognition. Experimental results on a Tephritidae dataset show that the three proposed discriminative coding methods perform well in insect recognition, and the proposed fusion scheme improves the recognition accuracy significantly.

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