The Identification of Context-Sensitive Features: A Formal Definition of Context for Concept Learning
<|reference_start|>The Identification of Context-Sensitive Features: A Formal Definition of Context for Concept Learning: A large body of research in machine learning is concerned with supervised learning from examples. The examples are typically represented as vectors in a multi-dimensional feature space (also known as attribute-value descriptions). A teacher partitions a set of training examples into a finite number of classes. The task of the learning algorithm is to induce a concept from the training examples. In this paper, we formally distinguish three types of features: primary, contextual, and irrelevant features. We also formally define what it means for one feature to be context-sensitive to another feature. Context-sensitive features complicate the task of the learner and potentially impair the learner's performance. Our formal definitions make it possible for a learner to automatically identify context-sensitive features. After context-sensitive features have been identified, there are several strategies that the learner can employ for managing the features; however, a discussion of these strategies is outside of the scope of this paper. The formal definitions presented here correct a flaw in previously proposed definitions. We discuss the relationship between our work and a formal definition of relevance.<|reference_end|>
@article{turney2002the,
    title={The Identification of Context-Sensitive Features: A Formal Definition of
  Context for Concept Learning},
    author={Peter D. Turney (National Research Council of Canada)},
    journal={13th International Conference on Machine Learning, Workshop on
  Learning in Context-Sensitive Domains, Bari, Italy, (1996), 53-59},
    year={2002},
    number={NRC-39222},
    archivePrefix={arXiv},
    eprint={cs/0212038},
    primaryClass={cs.LG cs.CV}
}