Archive for July 8th, 1997

Efficient Feature Selection in Conceptual Clustering

Feature selection has proven to be a valuable technique in supervised learning for improving predictive accuracy while reducing the number of attributes considered in a task. We investigate the potential for similar benefits in an unsupervised learning task, conceptual clustering. The issues raised in feature selection by the absence of class labels are discussed and an implementation of a sequential feature selection algorithm based on an existing conceptual clustering system is described. Additionally, we present a second implementation which employs a technique for improving the efficiency of the search for an optimal description and compare the performance of both algorithms.

Read the paper:

Efficient Feature Selection in Conceptual Clustering

by Mark Devaney, Ashwin Ram

14th  International Conference on Machine Learning (ICML-97), Nashville, TN, July 1997

Situation Development in a Complex Real-World Domain

Applying techniques from Machine Learning to real-world domains and problems often requires considerable processing of the input data, to both remove noise and to augment the amount and type of information present. We describe our work in the task of situation assessment in the domain of US Army training exercises involving hundreds of agents interacting in real-time over the course of several days. In particular, we describe techniques we have developed to process this data and draw general conclusions on the types of information required in order to apply various Machine Learning algorithms and how this information may be extracted in real-world situations where it is not directly represented.

Read the paper:

Situation Development in a Complex Real-World Domain

by Mark Devaney and Ashwin Ram

International Conference on Machine Learning (ICML-97) Workshop on Machine Learning Applications in the Real World, Nashville, TN, July 1997