Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]

Ren, Yongmao; Zhang, Le; Suganthan, Ponnuthurai Nagaratnam

doi:10.1109/mci.2015.2471235

Cited by 570 publications

(269 citation statements)

References 133 publications

Supporting

Mentioning

259

Contrasting

Unclassified

Order By: Relevance

“…A recent survey of randomized algorithms for training neural networks conducted by [14] provides an extensive review of the use of randomization in kernel machines and related fields. Furthermore, Ye et al [28] provide a systematic review and the state-of-the-art of the ensemble methods that can serve as a guideline for beginners and practitioners. They discussed the main theories associated with the ensemble classification and regression.…”

Section: Randomized and Ensemble Methodsmentioning

confidence: 99%

Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems

Hassan

Hamada

2017

Computation

View full text Add to dashboard Cite

Accuracy improvement is among the primary key research focuses in the area of recommender systems. Traditionally, recommender systems work on two sets of entities, Users and Items, to estimate a single rating that represents a user's acceptance of an item. This technique was later extended to multi-criteria recommender systems that use an overall rating from multi-criteria ratings to estimate the degree of acceptance by users for items. The primary concern that is still open to the recommender systems community is to find suitable optimization algorithms that can explore the relationships between multiple ratings to compute an overall rating. One of the approaches for doing this is to assume that the overall rating as an aggregation of multiple criteria ratings. Given this assumption, this paper proposed using feed-forward neural networks to predict the overall rating. Five powerful training algorithms have been tested, and the results of their performance are analyzed and presented in this paper.

show abstract

Section: Randomized and Ensemble Methodsmentioning

confidence: 99%

Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems

Hassan

Hamada

2017

Computation

View full text Add to dashboard Cite

show abstract

“…The final decision is obtained by weighted majority voting of the classes predicted by the individual classifier. There are many variants of the boosting algorithm such as AdaBoost, AdaBoost.M1, AdaBoost.M2, AdaBoost.R, Arcing and Real Adaboost [3][4][5][6]. We used AdaBoost.M1 in our experiments.…”

Section: Base Learners and Ensemble Techniquesmentioning

confidence: 99%

“…In the integration step, the outputs of the trained base classifiers are integrated to obtain a final decision. The main strategy in ensemble approach is therefore to generate many classifiers and integrate outputs of classifier such that the combination of classifiers improves the performance of a single classifier [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…Each base classifier must exhibit some level of diversity among themselves. The diversity can be achieved by mainly three approaches: data diversity, parameter diversity and structural diversity [6]. In data diversity, different training data subsets are generated from the original dataset for each of base classifier through re-sampling techniques.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The effectiveness of homogenous ensemble classifiers for Turkish and English texts

Kilimci

Akyokuş

Omurca

2016

2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)

View full text Add to dashboard Cite

Abstract-Text categorization has become more and more popular and important problem day by day because of the large proliferation of documents in many fields. To come up with this problem, several machine learning techniques are used for categorization such as naïve Bayes, support vector machines, artificial neural networks, etc. In this study, we concentrate on ensemble of multiple classifiers instead of using only a single one. We perform a comparative analysis of the impact of the ensemble techniques for text categorization domain. To carry out this, the same type of base classifiers but diversified training sets are used which is referred as homogenous ensembles. In order to diversify the training dataset, various ensemble algorithms are utilized such as Bagging, Boosting, Random Subspace and Random Forest. Multivariate Bernoulli Naïve Bayes is preferred as a base classifier due to its superior classification performance compared to the success of the other single classifiers. A wide range of comparative and extensive empirical studies are conducted on four widely-used datasets in text categorization domain in both Turkish and English. Finally, the effectiveness of ensemble algorithms is discussed.

show abstract

“…There exists a number of classification algorithms including Bayesian classifiers [12], nearest neighbor classifiers [11], rule-based classifiers [9], support vector machines [10], classification trees [6,26], neural classifiers [8,23], fuzzy logic-based classifiers [4,17,19] and many hybrid and ensemble methods [27,30].…”

Section: Introductionmentioning

confidence: 99%

Design of Fuzzy Rule-based Classifiers through Granulation and Consolidation

Riid

Preden

2017

Journal of Artificial Intelligence and Soft Computing Research

View full text Add to dashboard Cite

This paper addresses the issue how to strike a good balance between accuracy and compactness in classification systems -still an important question in machine learning and data mining. The fuzzy rule-based classification approach proposed in current paper exploits the method of rule granulation for error reduction and the method of rule consolidation for complexity reduction. The cooperative nature of those methods -the rules are split in a way that makes efficient rule consolidation feasible and rule consolidation itself is capable of further error reduction -is demonstrated in a number of experiments with nine benchmark classification problems. Further complexity reduction, if necessary, is provided by rule compression.

show abstract

Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]

Cited by 570 publications

References 133 publications

Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems

Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems

The effectiveness of homogenous ensemble classifiers for Turkish and English texts

Design of Fuzzy Rule-based Classifiers through Granulation and Consolidation

Contact Info

Product

Resources

About