Diversity in multiple classifier ensembles based on binary feature quantisation with application to face recognition

Sirlantzis, Konstantinos; Hoque, Sanaul; Fairhurst, Michael

doi:10.1016/j.asoc.2005.08.002

Cited by 27 publications

(14 citation statements)

References 12 publications

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“…Both theoretical and empirical researches have demonstrated that a good ensemble can not only improve generalization ability significantly, but also strengthen the robustness of the classification system. The EoCs has become a hotspot in machine learning and data mining [2] and been successfully applied in various application fields, including handwriting recognition [3][4][5][6], face recognition [7][8][9], finger-print verification [10,11], protein subcellular location prediction [12,13] and classification of biological data [14].…”

Section: Introductionmentioning

confidence: 99%

Dynamic weighting ensemble classifiers based on cross-validation

Zhu

Chen

et al. 2010

Neural Comput & Applic

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Ensemble of classifiers constitutes one of the main current directions in machine learning and data mining. It is accepted that the ensemble methods can be divided into static and dynamic ones. Dynamic ensemble methods explore the use of different classifiers for different samples and therefore may get better generalization ability than static ensemble methods. However, for most of dynamic approaches based on KNN rule, additional part of training samples should be taken out for estimating ''local classification performance'' of each base classifier. When the number of training samples is not sufficient enough, it would lead to the lower accuracy of the training model and the unreliableness for estimating local performances of base classifiers, so further hurt the integrated performance. This paper presents a new dynamic ensemble model that introduces cross-validation technique in the process of local performances' evaluation and then dynamically assigns a weight to each component classifier. Experimental results with 10 UCI data sets demonstrate that when the size of training set is not large enough, the proposed method can achieve better performances compared with some dynamic ensemble methods as well as some classical static ensemble approaches.

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Section: Introductionmentioning

confidence: 99%

Dynamic weighting ensemble classifiers based on cross-validation

Zhu

Chen

et al. 2010

Neural Comput & Applic

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“…Meanwhile, all s Θ k values are independent of one another but share the same distribution. For integrated algorithms, the difference of base classifiers can significantly affect their performance [29]. The two methods of RF randomness described below ensure significant difference among the base classifiers.…”

Section: Random Forestmentioning

confidence: 99%

A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest

Huang

2016

Energies

111

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Abstract:The prediction accuracy of short-term load forecast (STLF) depends on prediction model choice and feature selection result. In this paper, a novel random forest (RF)-based feature selection method for STLF is proposed. First, 243 related features were extracted from historical load data and the time information of prediction points to form the original feature set. Subsequently, the original feature set was used to train an RF as the original model. After the training process, the prediction error of the original model on the test set was recorded and the permutation importance (PI) value of each feature was obtained. Then, an improved sequential backward search method was used to select the optimal forecasting feature subset based on the PI value of each feature. Finally, the optimal forecasting feature subset was used to train a new RF model as the final prediction model. Experiments showed that the prediction accuracy of RF trained by the optimal forecasting feature subset was higher than that of the original model and comparative models based on support vector regression and artificial neural network.

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“…Another comprehensive approach to achieve diversity is by deploying different classifiers [31]. The instability of classifiers can be controlled by changing such parameters, hence resulting in diversity.…”

Section: Correlationmentioning

confidence: 99%

Ensemble learning methods for decision making: Status and future prospects

Ali

Tirumala

Sarrafzadeh

2015

2015 International Conference on Machine Learning and Cybernetics (ICMLC)

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Abstract:In real world situations every model has some weaknesses and will make errors on training data. Given the fact that each model has certain limitations, the aim of ensemble learning is to supervise their strengths and weaknesses, leading to best possible decision in general. Ensemble based machine learning is a solution of minimizing risk in decision making. Bagging, boosting, stacked generalization and mixture of expert methods are the most popular techniques to construct ensemble systems. For the purpose of combining outputs of class labels, weighted majority voting, behaviour knowledge space and border count methods are used to construct independent classifiers and to achieve diversity among the classifiers which is important in ensemble learning. It was found that an ideal ensemble method should work on the principal of achieving six paramount characteristics of ensemble learning; accuracy, scalability, computational cost, usability, compactness and speed of classification. In addition, the ideal ensemble method would be able to handle large huge image size and long term historical data particularly of spatial and temporal. In this paper we reveal that ensemble models have obtained high acceptability in terms of accuracy than single models. Further, we present an analogy of various ensemble techniques, their applicability, measuring the solution diversity, challenges and proposed methods to overcome these challenges without diverting from the original concepts.

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Diversity in multiple classifier ensembles based on binary feature quantisation with application to face recognition

Cited by 27 publications

References 12 publications

Dynamic weighting ensemble classifiers based on cross-validation

Dynamic weighting ensemble classifiers based on cross-validation

A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest

Ensemble learning methods for decision making: Status and future prospects

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