Vector quantization and signal compression
Vector quantization and signal compression
Neural Computation
Prior knowledge in support vector kernels
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Using domain knowledge in knowledge discovery
Proceedings of the eighth international conference on Information and knowledge management
Optimizing classifiers for imbalanced training sets
Proceedings of the 1998 conference on Advances in neural information processing systems II
Computational Economics - Computational Studies at Stanford
Explanation-Based Neural Network Learning: A Lifelong Learning Approach
Explanation-Based Neural Network Learning: A Lifelong Learning Approach
Machine Learning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Support Vector Machines for Classification in Nonstandard Situations
Machine Learning
Training Invariant Support Vector Machines
Machine Learning
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Incorporating prior knowledge with weighted margin support vector machines
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
EDDIE-automation, a decision support tool for financial forecasting
Decision Support Systems - Special issue: Data mining for financial decision making
Knowledge-Based Kernel Approximation
The Journal of Machine Learning Research
Bias Analysis in Text Classification for Highly Skewed Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Switching and Learning in Feedback Systems
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
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When dealing with real-world problems, there is considerable amount of prior domain knowledge that can provide insights on various aspect of the problem. On the other hand, many machine learning methods rely solely on the data sets for their learning phase and do not take into account any explicitly expressed domain knowledge. This paper proposes a framework that investigates and enables the incorporation of prior domain knowledge with respect to three key characteristics of inductive machine learning algorithms: consistency, generalization and convergence. The framework is used to review, classify and analyse key existing approaches to incorporating domain knowledge into inductive machine learning, as well as to consider the risks of doing so. The paper also demonstrates the design of a novel hierarchical semi-parametric machine learning method, capable of incorporating prior domain knowledge. The method-VQSVM-extends the support vector machine (SVM) family of methods with vector quantization (VQ) techniques to address the problem of learning from imbalanced data sets. The paper presents the results of testing the method on a collection of imbalanced data sets with various imbalance ratios and various numbers of subclasses. The learning process of the VQSVM method utilizes some domain knowledge to solve problem of fitting imbalance data. The experiments in the paper demonstrate that enabling the incorporation of prior domain knowledge into the SVM framework is an effective way to overcome the sensitivity of SVM towards the imbalance ratio in a data set.