Heterogeneous classifier ensemble with fuzzy rule-based meta learner

Nguyen, Tien Thanh; Nguyen, Mai P.; Pham, Xuan Cuong; Liew, Alan Wee‐Chung

doi:10.1016/j.ins.2017.09.009

Cited by 39 publications

(24 citation statements)

References 32 publications

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“…Finally, besides the two popular combiners i.e. Sum and Majority Vote [4,36], novel combining algorithms were introduced to enhance the task of combining on 18] used the Ordered Wei 37] proposed a new fusion scheme based on the upper integrals. Costa et al [38] used the generalized mixture functions as a combining algorithm in which the weight each classifier put on a class was set dynamically in the combination process.…”

Section: Ensemble Methodsmentioning

confidence: 99%

“…A combiner which can capture the facet of uncertainty outputs would be desirable. In the literature, several combiners have been introduced based on this consideration, such as fuzzy IF-THEN rule-based combiner [4] and Decision Template method [5]. In this study, we propose an ensemble framework based on modeling the uncertainty in the base classifiers using interval-based representations [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, in the heterogeneous ensemble systems, a fixed set of different learning algorithms learns on the same training set to generate the different base classifiers. The outputs of these classifiers (called meta-data of Level1 data) are then combined to make the final prediction [3][4][5]18]. In this type of ensembles, the approach is focused on designing algorithms that combine the meta-data to achieve higher accuracy than that using a single classifier.…”

Section: Introductionmentioning

confidence: 99%

“…The experiments on just one hand gesture dataset with 3 different sizes of the training set, however, do not present a convincing comparison. Recently, Nguyen et al[4] proposed a hybrid combining classifier system in which fuzzy rules work on the meta-data to produce the classification model. Although that system outperforms other fuzzy rules-based methods and ensemble methods in the experiment since the uncertainty in the meta-data can be captured by the fuzzy rules, the training process has high time complexity than other training combining methods due…”

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confidence: 99%

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Combining heterogeneous classifiers via granular prototypes

Nguyen

Pham

et al. 2018

Applied Soft Computing

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In this study, a novel framework to combine multiple classifiers in an ensemble system is introduced. Here we exploit the concept of information granule to construct granular prototypes for each class on the outputs of an ensemble of base classifiers. In the proposed method, uncertainty in the outputs of the base classifiers on training observations is captured by an interval-based representation. To predict the class label for a new observation, we first determine the distances between the output of the base classifiers for this observation and the class prototypes, then the predicted class label is obtained by choosing the label associated with the shortest distance. In the experimental study, we combine several learning algorithms to build the ensemble system and conduct experiments on the UCI, colon cancer, and selected CLEF2009 datasets. The experimental results demonstrate that the proposed framework outperforms several benchmarked algorithms including two trainable combining methods, i.e.

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Section: Ensemble Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Combining heterogeneous classifiers via granular prototypes

Nguyen

Pham

et al. 2018

Applied Soft Computing

Self Cite

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“…Nanni et al [46] proposed an ensemble of SVM base classifiers for diagnosis of Alzheimer's disease. Nguyen et al [47] heterogeneous ensemble classifier combined with a fuzzy IF-THEN rule inference engine to capture the uncertainty in the outputs of the base classifiers. Tama and Fitri [39] utilized the AdaBoost.M1 algorithm [48] to combine SVM, C4.5, and NB.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Medical Decision–Support Framework Based on a Heterogeneous Ensemble Classifier for Diabetes Prediction

et al. 2019

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Early diagnosis of diabetes mellitus (DM) is critical to prevent its serious complications. An ensemble of classifiers is an effective way to enhance classification performance, which can be used to diagnose complex diseases, such as DM. This paper proposes an ensemble framework to diagnose DM by optimally employing multiple classifiers based on bagging and random subspace techniques. The proposed framework combines seven of the most suitable and heterogeneous data mining techniques, each with a separate set of suitable features. These techniques are k-nearest neighbors, naïve Bayes, decision tree, support vector machine, fuzzy decision tree, artificial neural network, and logistic regression. The framework is designed accurately by selecting, for every sub-dataset, the most suitable feature set and the most accurate classifier. It was evaluated using a real dataset collected from electronic health records of Mansura University Hospitals (Mansura, Egypt). The resulting framework achieved 90% of accuracy, 90.2% of recall = 90.2%, and 94.9% of precision. We evaluated and compared the proposed framework with many other classification algorithms. An analysis of the results indicated that the proposed ensemble framework significantly outperforms all other classifiers. It is a successful step towards constructing a personalized decision support system, which could help physicians in daily clinical practice.

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Sentiment classification in social media data by combining triplet belief functions

2021

Asso for Info Science & Tech

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Sentiment analysis is an emerging technique that caters for semantic orientation and opinion mining. It is increasingly used to analyse online reviews and posts for identifying people's opinions and attitudes to products and events in order to improve business performance of companies and aid to make better organising strategies of events. This paper presents an innovative approach to combining the outputs of sentiment classifiers under the framework of belief functions. It consists of the formulation of sentiment classifier outputs in the triplet evidence structure and the development of general formulas for combining triplet functions derived from sentiment classification results via three evidential combination rules along with comparative analyses. The empirical studies have been conducted on examining the effectiveness of our method for sentiment classification individually and in combination, and the results demonstrate that the best combined classifiers by our method outperforms the best individual classifiers over five review datasets.

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Heterogeneous classifier ensemble with fuzzy rule-based meta learner

Cited by 39 publications

References 32 publications

Combining heterogeneous classifiers via granular prototypes

Combining heterogeneous classifiers via granular prototypes

A Comprehensive Medical Decision–Support Framework Based on a Heterogeneous Ensemble Classifier for Diabetes Prediction

Sentiment classification in social media data by combining triplet belief functions

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