Applied multivariate statistical analysis
Applied multivariate statistical analysis
The Strength of Weak Learnability
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
How to Make AdaBoost.M1 Work for Weak Base Classifiers by Changing Only One Line of the Code
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Comparing Pure Parallel Ensemble Creation Techniques Against Bagging
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A new ensemble diversity measure applied to thinning ensembles
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
IEEE Transactions on Knowledge and Data Engineering
A local boosting algorithm for solving classification problems
Computational Statistics & Data Analysis
Classifier ensemble selection using hybrid genetic algorithms
Pattern Recognition Letters
RotBoost: A technique for combining Rotation Forest and AdaBoost
Pattern Recognition Letters
Ensembles of Multi-Objective Decision Trees
ECML '07 Proceedings of the 18th European conference on Machine Learning
Empirical analysis of support vector machine ensemble classifiers
Expert Systems with Applications: An International Journal
A novel method for constructing ensemble classifiers
Statistics and Computing
MTForest: Ensemble Decision Trees based on Multi-Task Learning
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Particle swarm optimization based multi-prototype ensembles
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Computational Statistics & Data Analysis
Recruiter selection model and implementation within the united states army
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Indexing ICD-9 codes for free-textual clinical diagnosis records by a new ensemble classifier
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Artificial Intelligence Review
International Journal of Hybrid Intelligent Systems - Hybrid Fuzzy Models
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Using ensembles of decision trees to automate repetitive tasks in web applications
Proceedings of the 2nd ACM SIGCHI symposium on Engineering interactive computing systems
International Journal of Approximate Reasoning
Mining data with random forests: A survey and results of new tests
Pattern Recognition
An empirical study of applying ensembles of heterogeneous classifiers on imperfect data
PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
Analysis of bagging ensembles of fuzzy models for premises valuation
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Comparison of bagging, boosting and stacking ensembles applied to real estate appraisal
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Detecting and ordering salient regions
Data Mining and Knowledge Discovery
Small-sample error estimation for bagged classification rules
EURASIP Journal on Advances in Signal Processing - Special issue on genomic signal processing
Machine learning approaches for high-resolution urban land cover classification: a comparative study
Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications
Anomaly detection using ensembles
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Compact ensemble trees for imbalanced data
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Random feature weights for decision tree ensemble construction
Information Fusion
Bucket Learning: Improving model quality through enhancing local patterns
Knowledge-Based Systems
Hellinger distance decision trees are robust and skew-insensitive
Data Mining and Knowledge Discovery
Classifiers selection in ensembles using genetic algorithms for bankruptcy prediction
Expert Systems with Applications: An International Journal
Generalised bottom-up pruning: A model level combination of decision trees
Expert Systems with Applications: An International Journal
Ensemble pruning using harmony search
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
An efficient ensemble classification method based on novel classifier selection technique
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
Pattern Recognition Letters
Scalable random forests for massive data
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
An expandable recommendation system on IPTV
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
Exploring topic coherence over many models and many topics
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
How large should ensembles of classifiers be?
Pattern Recognition
The use of artificial-intelligence-based ensembles for intrusion detection: a review
Applied Computational Intelligence and Soft Computing
Bagging and Boosting statistical machine translation systems
Artificial Intelligence
Classifying Very High-Dimensional Data with Random Forests Built from Small Subspaces
International Journal of Data Warehousing and Mining
Decision trees: a recent overview
Artificial Intelligence Review
Pattern Recognition
Malware detection by pruning of parallel ensembles using harmony search
Pattern Recognition Letters
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We experimentally evaluate bagging and seven other randomization-based approaches to creating an ensemble of decision tree classifiers. Statistical tests were performed on experimental results from 57 publicly available data sets. When cross-validation comparisons were tested for statistical significance, the best method was statistically more accurate than bagging on only eight of the 57 data sets. Alternatively, examining the average ranks of the algorithms across the group of data sets, we find that boosting, random forests, and randomized trees are statistically significantly better than bagging. Because our results suggest that using an appropriate ensemble size is important, we introduce an algorithm that decides when a sufficient number of classifiers has been created for an ensemble. Our algorithm uses the out-of-bag error estimate, and is shown to result in an accurate ensemble for those methods that incorporate bagging into the construction of the ensemble.