C4.5: programs for machine learning
C4.5: programs for machine learning
The weighted majority algorithm
Information and Computation
Machine Learning
Pruning Algorithms for Rule Learning
Machine Learning
Artificial Intelligence Review - Special issue on lazy learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Lazy learning
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning - Special issue on learning with probabilistic representations
Top-down induction of first-order logical decision trees
Artificial Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Machine learning from examples: inductive and lazy methods
Data & Knowledge Engineering - Special jubilee issue: DKE 25
Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning - Special issue on context sensitivity and concept drift
On-line learning in neural networks
On-line learning in neural networks
Learning in graphical models
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Inductive logic programming: issues, results and the challenge of learning language in logic
Artificial Intelligence - Special issue on applications of artificial intelligence
ACM Computing Surveys (CSUR)
General and Efficient Multisplitting of Numerical Attributes
Machine Learning
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Using analytic QP and sparseness to speed training of support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Information complexity of neural networks
Neural Networks
Constructing X-of-N Attributes for Decision Tree Learning
Machine Learning
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Lazy Learning of Bayesian Rules
Machine Learning
Machine Learning
Radial basis function networks 2: new advances in design
Radial basis function networks 2: new advances in design
Complexity and expressive power of logic programming
ACM Computing Surveys (CSUR)
Advances in Inductive Logic Programming
Advances in Inductive Logic Programming
Machine Learning
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Instance Selection and Construction for Data Mining
Instance Selection and Construction for Data Mining
Relational Data Mining
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Feature Generation Using General Constructor Functions
Machine Learning
FERNN: An Algorithm for Fast Extraction of Rules fromNeural Networks
Applied Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
RainForest—A Framework for Fast Decision Tree Construction of Large Datasets
Data Mining and Knowledge Discovery
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
A Unifying View on Instance Selection
Data Mining and Knowledge Discovery
Convergence of a Generalized SMO Algorithm for SVM Classifier Design
Machine Learning
A perspective view and survey of meta-learning
Artificial Intelligence Review
Combining Classifiers with Meta Decision Trees
Machine Learning
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Artificial Neural Network Learning: A Comparative Review
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Effects of domain characteristics on instance-based learning algorithms
Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
Inference for the Generalization Error
Machine Learning
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Online learning of linear classifiers
Advanced lectures on machine learning
Classes of kernels for machine learning: a statistics perspective
The Journal of Machine Learning Research
Optimal structure identification with greedy search
The Journal of Machine Learning Research
An introduction to variable and feature selection
The Journal of Machine Learning Research
On Data and Algorithms: Understanding Inductive Performance
Machine Learning
The Knowledge Engineering Review
Simplifying decision trees: A survey
The Knowledge Engineering Review
Rule extraction: using neural networks or for neural networks?
Journal of Computer Science and Technology
A classification paradigm for distributed vertically partitioned data
Neural Computation
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
The class imbalance problem: A systematic study
Intelligent Data Analysis
Journal of Artificial Intelligence Research
Issues in stacked generalization
Journal of Artificial Intelligence Research
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Constructing diverse classifier ensembles using artificial training examples
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Agent-based distributed data mining: the KDEC scheme
Intelligent information agents
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Naive bayes classifiers that perform well with continuous variables
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Explaining the output of ensembles in medical decision support on a case by case basis
Artificial Intelligence in Medicine
On connectionism, rule extraction, and brain-like learning
IEEE Transactions on Fuzzy Systems
An iterative pruning algorithm for feedforward neural networks
IEEE Transactions on Neural Networks
Constructive neural-network learning algorithms for pattern classification
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Fitting a graph to vector data
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A survey of prediction models for breast cancer survivability
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Spatial statistics of visual keypoints for texture recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Long term cardiovascular risk models' combination
Computer Methods and Programs in Biomedicine
A robust learning model for dealing with missing values in many-core architectures
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Distributed learning with data reduction
Transactions on computational collective intelligence IV
A new collaborative filtering recommendation approach based on naive Bayesian method
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
An incremental ensemble of classifiers
Artificial Intelligence Review
Histogram based color object classification by multi-class support vector machine
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
On achieving semi-supervised pattern recognition by utilizing tree-based SOMs
Pattern Recognition
Parallel multi-objective Ant Programming for classification using GPUs
Journal of Parallel and Distributed Computing
Robust predictive model for evaluating breast cancer survivability
Engineering Applications of Artificial Intelligence
An organ allocation system for liver transplantation based on ordinal regression
Applied Soft Computing
Artificial Intelligence in Medicine
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Supervised classification is one of the tasks most frequently carried out by so-called Intelligent Systems. Thus, a large number of techniques have been developed based on Artificial Intelligence (Logic-based techniques, Perceptron-based techniques) and Statistics (Bayesian Networks, Instance-based techniques). The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various classification algorithms and the recent attempt for improving classification accuracy--ensembles of classifiers.