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Classifier Ensembles: Efficient Techniques to Define Robust System Structures
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Classifier Ensembles: Efficient Techniques to Define Robust System Structures

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".

Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 11598

Special Issue Editor

Dr. Anne Canuto

E-Mail Website
Guest Editor
Department of Informatics and Applied Mathematics (DIMAp), Federal University of Rio Grande do Norte (UFRN), Natal 59078-970, Brazil
Interests: classifier ensemble; optimization techniques; deep learning; multi-label classification

Special Issue Information

Dear Colleagues,

One of the main challenges in the design of efficient classifier ensembles is the process of selecting their structure, especially with regard to individual classifiers with their respective parameters as well as combination methods. One way to improve the performance of classifier ensembles is by automatically selecting the best classifiers and combination method to compose their structure. This selection can be done statically or dynamically. In the static selection, once the ensemble structure is defined, all test instances will be classified by the same structure. In other words, the ensemble structure is selected before starting the classification step and used for all instances of this step. This selection is the most traditional and this problem has been treated as a meta-learning problem, automatic selection of machine learning (auto-ML) or an optimization problem. Recently, based on the assumption that every test instance has particularities and different levels of difficulties in the classification process, the dynamic selection approach has arisen. In this selection, each instance is classified by a different ensemble structure (set of individual classifiers and method of combination). Thus, in order to classify N instances it is necessary to set N ensemble structures, one for each test instance, selecting the most appropriate structure to classify one specific instance. In dynamic selection, the selection of the ensemble structure is performed during the testing phase.

Despite the high number of studies and techniques, finding an optimal parameter set that maximizes classification accuracy of an ensemble system is still an open problem. The search space for all parameters of an ensemble system (classifier type, size, classifier parameters, combination method and feature selection) is very large. This Special Issue on “Classifier Ensembles: Efficient Techniques to Define Robust System Structures” aims to promote new theories, techniques, and methods with which to exploit the selection of efficient ensemble structures, including both selection approaches, static and dynamic selections.

Dr. Anne Canuto
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Classifier Ensembles
  • Optimization Techniques
  • Meta-learning
  • Dynamic Selection in Classifier Ensembles

Published Papers (3 papers)

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Research

15 pages, 1709 KiB  
Article
A Review Structure Based Ensemble Model for Deceptive Review Spam
by Zhi-Yuan Zeng, Jyun-Jie Lin, Mu-Sheng Chen, Meng-Hui Chen, Yan-Qi Lan and Jun-Lin Liu
Information 2019, 10(7), 243; https://doi.org/10.3390/info10070243 - 17 Jul 2019
Cited by 25 | Viewed by 3946
Abstract
Consumers’ purchase behavior increasingly relies on online reviews. Accordingly, there are more and more deceptive reviews which are harmful to customers. Existing methods to detect spam reviews mainly take the problem as a general text classification task, but they ignore the important features [...] Read more.
Consumers’ purchase behavior increasingly relies on online reviews. Accordingly, there are more and more deceptive reviews which are harmful to customers. Existing methods to detect spam reviews mainly take the problem as a general text classification task, but they ignore the important features of spam reviews. In this paper, we propose a novel model, which splits a review into three parts: first sentence, middle context, and last sentence, based on the discovery that the first and last sentence express stronger emotion than the middle context. Then, the model uses four independent bidirectional long-short term memory (LSTM) models to encode the beginning, middle, end of a review and the whole review into four document representations. After that, the four representations are integrated into one document representation by a self-attention mechanism layer and an attention mechanism layer. Based on three domain datasets, the results of in-domain and mix-domain experiments show that our proposed method performs better than the compared methods. Full article
(This article belongs to the Special Issue Classifier Ensembles: Efficient Techniques to Define Robust System Structures)
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17 pages, 1123 KiB  
Article
Coupled Least Squares Support Vector Ensemble Machines
by Dickson Keddy Wornyo and Xiang-Jun Shen
Information 2019, 10(6), 195; https://doi.org/10.3390/info10060195 - 03 Jun 2019
Cited by 4 | Viewed by 3011
Abstract
The least squares support vector method is a popular data-driven modeling method which shows better performance and has been successfully applied in a wide range of applications. In this paper, we propose a novel coupled least squares support vector ensemble machine (C-LSSVEM). The [...] Read more.
The least squares support vector method is a popular data-driven modeling method which shows better performance and has been successfully applied in a wide range of applications. In this paper, we propose a novel coupled least squares support vector ensemble machine (C-LSSVEM). The proposed coupling ensemble helps improve robustness and produce good classification performance than the single model approach. The proposed C-LSSVEM can choose appropriate kernel types and their parameters in a good coupling strategy with a set of classifiers being trained simultaneously. The proposed method can further minimize the total loss of ensembles in kernel space. Thus, we form an ensemble regressor by co-optimizing and weighing base regressors. Experiments conducted on several datasets such as artificial datasets, UCI classification datasets, UCI regression datasets, handwritten digits datasets and NWPU-RESISC45 datasets, indicate that C-LSSVEM performs better in achieving the minimal regression loss and the best classification accuracy relative to selected state-of-the-art regression and classification techniques. Full article
(This article belongs to the Special Issue Classifier Ensembles: Efficient Techniques to Define Robust System Structures)
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14 pages, 1610 KiB  
Article
Efficient Ensemble Classification for Multi-Label Data Streams with Concept Drift
by Yange Sun, Han Shao and Shasha Wang
Information 2019, 10(5), 158; https://doi.org/10.3390/info10050158 - 28 Apr 2019
Cited by 14 | Viewed by 4333
Abstract
Most existing multi-label data streams classification methods focus on extending single-label streams classification approaches to multi-label cases, without considering the special characteristics of multi-label stream data, such as label dependency, concept drift, and recurrent concepts. Motivated by these challenges, we devise an efficient [...] Read more.
Most existing multi-label data streams classification methods focus on extending single-label streams classification approaches to multi-label cases, without considering the special characteristics of multi-label stream data, such as label dependency, concept drift, and recurrent concepts. Motivated by these challenges, we devise an efficient ensemble paradigm for multi-label data streams classification. The algorithm deploys a novel change detection based on Jensen–Shannon divergence to identify different kinds of concept drift in data streams. Moreover, our method tries to consider label dependency by pruning away infrequent label combinations to enhance classification performance. Empirical results on both synthetic and real-world datasets have demonstrated its effectiveness. Full article
(This article belongs to the Special Issue Classifier Ensembles: Efficient Techniques to Define Robust System Structures)
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