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 thes...

• Type: Journal
• Year: 2002
• Where: CORR
• Authors: Peter D. Turney
• Keywords: Context-sensitive Features, CORR 2002, Education, Multi-dimensional Feature Space, Training Examples