A Weighted Voting Classifier Based on Differential Evolution

Zhang, Yong; Zhang, Hongrui; Cai, Jing; Yang, Baofeng

doi:10.1155/2014/376950

Cited by 68 publications

(60 citation statements)

References 18 publications

(25 reference statements)

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“…C is a diagonal matrix with diagonal elements C r , 1 r l, which are defined as below (from second order Taylor approximation): C r = γ r (4u 2 r + 8u 3 r + 4u 4 r + β u r + 2β u 3 r + β u 5 r ) 2(u 3 r + u 2 r + u r + 1) 2 (19) where u r = e w r γ r . Here, β is a positive constant, γ r = γ(| w r |+ε) −1 is always positive since γ > 0, and u r is always positive since w r γ r 0.…”

Section: Resultsmentioning

confidence: 99%

“…Zhang et al [3] proposed Differential Evolution for finding suitable weights for ensemble base classifiers. Similar to most heuristic solution techniques, they did not explicitly define cost function, but use classification accuracy for fitness function.…”

Section: Related Work: Ensembles That Combine Pre-trained Classifiersmentioning

confidence: 99%

“…To put it in another way, similar classifiers do not contribute to overall accuracy very much. This deficiency can be removed by increasing the classifier diversity [1,3,4]. Therefore, finding new diversity measurements [5] and improving existing ones [4] are an ongoing research effort in ensemble studies.…”

Section: Introductionmentioning

confidence: 99%

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Sparsity-driven weighted ensemble classifier

Özgür

Erdem²,

Nar³

2018

IJCIS

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In this study, a novel sparsity-driven weighted ensemble classifier (SDWEC) that improves classification accuracy and minimizes the number of classifiers is proposed. Using pre-trained classifiers, an ensemble in which base classifiers votes according to assigned weights is formed. These assigned weights directly affect classifier accuracy. In the proposed method, ensemble weights finding problem is modeled as a cost function with the following terms: (a) a data fidelity term aiming to decrease misclassification rate, (b) a sparsity term aiming to decrease the number of classifiers, and (c) a non-negativity constraint on the weights of the classifiers. As the proposed cost function is non-convex thus hard to solve, convex relaxation techniques and novel approximations are employed to obtain a numerically efficient solution. Sparsity term of cost function allows trade-off between accuracy and testing time when needed. The efficiency of SDWEC was tested on 11 datasets and compared with the state-of-the art classifier ensemble methods. The results show that SDWEC provides better or similar accuracy levels using fewer classifiers and reduces testing time for ensemble.

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Section: Resultsmentioning

confidence: 99%

Section: Related Work: Ensembles That Combine Pre-trained Classifiersmentioning

confidence: 99%

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Sparsity-driven weighted ensemble classifier

Özgür

Erdem²,

Nar³

2018

IJCIS

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“…The main aim of the ensemble learning is to weigh several individual classifiers and combine the prediction of the multiple classifiers, which outperforms prediction of individual classifiers [31]. The majority voting and weighted majority voting are the most popular combination schemas, which are widely used in ensemble classification [39]. The simple majority voting schema selects one of many alternatives of the predicted classes with the most votes [40].…”

Section: F Ensemble Classifiersmentioning

confidence: 99%

Automatic Irony Detection using Feature Fusion and Ensemble Classifier

Kumar¹,

Harish²

2019

IJIMAI

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With the advent of micro-blogging sites, users are pioneer in expressing their sentiments and emotions on global issues through text. Automatic detection and classification of sentiments like sarcastic or ironic content in microblogging reviews is a challenging task. It requires a system that manages some kind of knowledge to interpret the sentiment expressed in text. The available approaches are quite limited in their capabilities and scope to detect ironic utterances present in the text. In this regards, the paper propose feature fusion to provide knowledge to the system by alternative sets of features obtained using linguistic and content based text features. The proposed work extracts five sets of linguistic features and fuses with features selected using two stages of a feature selection method. In order to demonstrate the effectiveness of the proposed method, we conduct extensive experimentation by selecting different feature subsets. The performances of the proposed method are evaluated using Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT) and ensemble classifiers. The experimental result shows the proposed approach significantly out-performs the conventional methods.

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“…We identified the favorable weights of all base classifiers using the approaches presented in Ref. 39. The main steps are given in Table 2.…”

Section: Classification System Constructionmentioning

confidence: 99%

Terrain classification of polarimetric synthetic aperture radar imagery based on polarimetric features and ensemble learning

Huang

2017

J. Appl. Remote Sens

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Chuanbo Huang, "Terrain classification of polarimetric synthetic aperture radar imagery based on polarimetric features and ensemble learning," J. Appl. Remote Sens. 11(2), 026002 (2017), doi: 10.1117/1.JRS.11.026002. Abstract. An evolutionary classification system for terrain classification of polarimetric synthetic aperture radar (PolSAR) imagery based on ensemble learning with polarimetric and texture features is proposed. Polarimetric measurements cannot produce sufficient identification information for PolSAR terrain classification in some complex areas. To address this issue, texture features have been successfully used in image segmentation. The system classification feature has been adopted using a combination of Pauli features and the last principal component of Gabor texture-feature dimensionality reduction. The resulting feature combination assigned through experimental analysis is very suitable for describing structural and spatial information. To obtain a good integration effect, the basic classifier should be as precise as possible and the differences among the features should be as distinct as possible. We therefore examine and construct an ensemble-weighted voting classifier, including two support vector machine models that are constructed using kernel functions of the radial basis and sigmoid, extreme learning machine, k-nearest neighbor, and discriminant analysis classifier, which can avoid redundancy and bias because of different theoretical backgrounds. An experiment was performed to estimate the proposed algorithm's performance. The results verified that the algorithm can obtain better accuracy than the four classifiers mentioned in this paper.

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A Weighted Voting Classifier Based on Differential Evolution

Cited by 68 publications

References 18 publications

Sparsity-driven weighted ensemble classifier

Sparsity-driven weighted ensemble classifier

Automatic Irony Detection using Feature Fusion and Ensemble Classifier

Terrain classification of polarimetric synthetic aperture radar imagery based on polarimetric features and ensemble learning

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