Incremental learning of concept descriptions: A method and experimental results
Machine intelligence 11
C4.5: programs for machine learning
C4.5: programs for machine learning
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Selecting Examples for Partial Memory Learning
Machine Learning
Mining long sequential patterns in a noisy environment
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Machine Learning
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Tree induction vs. logistic regression: a learning-curve analysis
The Journal of Machine Learning Research
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Accurate decision trees for mining high-speed data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
A note on the utility of incremental learning
AI Communications
Incremental Algorithms for Hierarchical Classification
The Journal of Machine Learning Research
An adaptive prequential learning framework for bayesian network classifiers
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Incremental algorithm driven by error margins
DS'06 Proceedings of the 9th international conference on Discovery Science
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Improving Adaptive Bagging Methods for Evolving Data Streams
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Incremental learning with multiple classifier systems using correction filters for classification
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
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Classification is a quite relevant task within data analysis field. This task is not a trivial task and different difficulties can arise depending on the nature of the problem. All these difficulties can become worse when the datasets are too large or when new information can arrive at any time. Incremental learning is an approach that can be used to deal with the classification task in these cases. It must alleviate, or solve, the problem of limited time and memory resources. One emergent approach uses concentration bounds to ensure that decisions are made when enough information supports them. IADEM is one of the most recent algorithms that use this approach. The aim of this paper is to improve the performance of this algorithm in different ways: simplifying the complexity of the induced models, adding the ability to deal with continuous data, improving the detection of noise, selecting new criteria for evolutionating the model, including the use of more powerful prediction techniques, etc. Besides these new properties, the new system, IADEM-2, preserves the ability to obtain a performance similar to standard learning algorithms independently of the datasets size and it can incorporate new information as the basic algorithm does: using short time per example.