instance-based learning

During a project examining the use of machine learning techniques for oil spill detection, we encountered several essential questions that we believe deserve the attention of the research community. We use our particular case study to illustrate such issues as problem formulation, selection of evaluation measures, and data preparation. We relate these issues to properties of the oil spill application, such as its imbalanced class distribution, that are shown to be common to many applications. Our solutions to these issues are implemented in the Canadian Environmental Hazards Detection System (CEHDS), which is about to undergo field testing.

On-line learning in domains where the target concept depends on some hidden context poses serious problems. A changing context can induce changes in the target concepts, producing what is known as concept drift. We describe a family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear. The general approach underlying all these algorithms consists of (1) keeping only a window of currently trusted examples and hypotheses; (2) storing concept descriptions and re-using them when a previous context re-appears; and (3) controlling both of these functions by a heuristic that constantly monitors the system's behavior. The paper reports on experiments that test the systems' performance under various conditions such as different levels of noise and different extent and rate of concept drift.

For real-world concept learning problems, feature selection is important to speed up learning and to improve concept quality. We review and analyze past approaches to feature selection and note their strengths and weaknesses. We then introduce and theoretically examine a new algorithm Relief which selects relevant features using a statistical method. Relief does not depend on heuristics, is accurate even if features interact, and is noise-tolerant. It requires only linear time in the number of given features and the number of training instances, regardless of the target concept complexity. The algorithm also has certain limitations such as non-optimal feature set size. Ways to overcome the limitations are suggested. We also report the test results of comparison between Relief and other feature selection algorithms. The empirical results support the theoretical analysis, suggesting a practical approach to feature selection for real-world problems.