Troika - An improved stacking schema for classification tasks

Authors:
Eitan Menahem;Lior Rokach;Yuval Elovici
Affiliations:
Department of Information Systems Engineering, Ben-Gurion University and Deutsche Telekom Laboratories at Ben-Gurion University, Be'er Sheva 84105, Israel;Department of Information Systems Engineering, Ben-Gurion University and Deutsche Telekom Laboratories at Ben-Gurion University, Be'er Sheva 84105, Israel;Department of Information Systems Engineering, Ben-Gurion University and Deutsche Telekom Laboratories at Ben-Gurion University, Be'er Sheva 84105, Israel
Venue:
Information Sciences: an International Journal
Year:
2009

Citing 23
Cited 11

Rule induction with CN2: some recent improvements

EWSL-91 Proceedings of the European working session on learning on Machine learning
A training algorithm for optimal margin classifiers

COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
C4.5: programs for machine learning

C4.5: programs for machine learning
Bagging predictors

Machine Learning
Robust Classification for Imprecise Environments

Machine Learning
Using Correspondence Analysis to Combine Classifiers

Machine Learning
Pairwise Classification as an Ensemble Technique

ECML '02 Proceedings of the 13th European Conference on Machine Learning
How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness

ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
An Evaluation of Grading Classifiers

IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Is Combining Classifiers with Stacking Better than Selecting the Best One?

Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets

The Journal of Machine Learning Research
Decision-tree instance-space decomposition with grouped gain-ratio

Information Sciences: an International Journal
Data-driven decomposition for multi-class classification

Pattern Recognition
A neural network ensemble method with jittered training data for time series forecasting

Information Sciences: an International Journal
Improving malware detection by applying multi-inducer ensemble

Computational Statistics & Data Analysis
Incremental construction of classifier and discriminant ensembles

Information Sciences: an International Journal
Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography

Computational Statistics & Data Analysis
Solving multiclass learning problems via error-correcting output codes

Journal of Artificial Intelligence Research
Issues in stacked generalization

Journal of Artificial Intelligence Research
Estimating continuous distributions in Bayesian classifiers

UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Task decomposition and module combination based on class relations: a modular neural network for pattern classification

IEEE Transactions on Neural Networks
Efficient classification for multiclass problems using modular neural networks

IEEE Transactions on Neural Networks

Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography

Computational Statistics & Data Analysis
Privacy-preserving data mining: A feature set partitioning approach

Information Sciences: an International Journal
An experimental study of one- and two-level classifier fusion for different sample sizes

Pattern Recognition Letters
Classification by cluster analysis: a new meta-learning based approach

MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
An incremental ensemble of classifiers

Artificial Intelligence Review
Multi-instance multi-label learning based on Gaussian process with application to visual mobile robot navigation

Information Sciences: an International Journal
A meta learning approach to grammatical error correction

ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
The use of artificial-intelligence-based ensembles for intrusion detection: a review

Applied Computational Intelligence and Soft Computing
Combining one-class classifiers via meta learning

Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
ACTIDS: an active strategy for detecting and localizing network attacks

Proceedings of the 2013 ACM workshop on Artificial intelligence and security
A niching genetic programming-based multi-objective algorithm for hybrid data classification

Neurocomputing

Quantified Score

Hi-index	0.07

Visualization

Abstract

Stacking is a general ensemble method in which a number of base classifiers are combined using one meta-classifier which learns their outputs. Such an approach provides certain advantages: simplicity; performance that is similar to the best classifier; and the capability of combining classifiers induced by different inducers. The disadvantage of stacking is that on multiclass problems, stacking seems to perform worse than other meta-learning approaches. In this paper we present Troika, a new stacking method for improving ensemble classifiers. The new scheme is built from three layers of combining classifiers. The new method was tested on various datasets and the results indicate the superiority of the proposed method to other legacy ensemble schemes, Stacking and StackingC, especially when the classification task consists of more than two classes.