This paper investigates two feature-scoring criteria that make use of estimated class probabilities: one method proposed by \citet{shen} and a complementary approach proposed below. We develop a theoretical framework to analyze each criterion and show that both estimate the spread (across all values of a given feature) of the probability that an example belongs to the positive class. Based on our analysis, we predict when each scoring technique will be advantageous over the other and give empirical results validating our predictions.

The Fifteenth National Conference on Artificial Intelligence (AAAI-98) was held in Madison, Wisconsin, on 26-30 July. The following four workshops were held in conjunction with the conference: (1) Case-Based Reasoning Integrations, (2) Learning for Text Categorization, (3) Predicting the Future: AI Approaches to Time-Series Problems, and (4) Software Tools for Developing Agents.

The immense growth of the web has caused the amount of text available online to skyrocket. The AAAI-98 Workshop on Learning for Text Categorization brought together researchers from many of respective areas. A to share their different experiences four workshops were held in conjunction final panel on the synergistic effects of in tackling similar problems. Specifically, several researchers made tasks, no previous workshop soning system, what the significance the point that making use of linguistic attempted to characterize CBR integration of these synergies is, how they can be structure, as well as using stylistic and issues. This nontextual features of documents, can Workshop highlights included panel and the other discussion periods improve categorization performance.