Learning time-varying concepts
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Estimating the Intrinsic Dimension of Data with a Fractal-Based Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving Medical/Biological Data Classification Performance by Wavelet Preprocessing
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Introduction to the Special Issue on Meta-Learning
Machine Learning
Data Mining: Concepts and Techniques
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Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Evaluating the intrinsic dimension of evolving data streams
Proceedings of the 2006 ACM symposium on Applied computing
Pattern Recognition, Third Edition
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The lack of a priori distinctions between learning algorithms
Neural Computation
The existence of a priori distinctions between learning algorithms
Neural Computation
On learning algorithm selection for classification
Applied Soft Computing
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ACM SIGKDD Explorations Newsletter
The Effect of History on Modeling Systems' Performance: The Problem of the Demanding Lord
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
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Machine learning algorithms perform differently in settings with varying levels of training set mislabeling noise. Therefore, the choice of a good algorithm for a particular learning problem is crucial. In this paper, we introduce the "Sigmoid Rule" Framework focusing on the description of classifier behavior in noisy settings. The framework uses an existing model of the expected performance of learning algorithms as a sigmoid function of the signal-to-noise ratio in the training instances. We study the parameters of the above sigmoid function using five different classifiers, namely, Naive Bayes, kNN, SVM, a decision tree classifier, and a rule-based classifier. Our study leads to the definition of intuitive criteria based on the sigmoid parameters that can be used to compare the behavior of learning algorithms in the presence of varying levels of noise. Furthermore, we show that there exists a connection between these parameters and the characteristics of the underlying dataset, hinting at how the inherent properties of a dataset affect learning. The framework is applicable to concept drift scenaria, including modeling user behavior over time, and mining of noisy data series, as in sensor networks.