Inductive Classifier Learning From Data
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Inductive Classifier Learning from Data
Abstract: "A central problem in artificial intelligence is reasoning under uncertainty. This thesis views inductive learning as reasoning under uncertainty and develops an Extended Bayesian Belief Function approach that allows a two-layer representation of the probabilistic rules: basic probabilistic belief and their confidences, which are independent of each other and represent different semantics of the rules. The use of the confidence measure of probabilistic rules can thus handle many difficult problems in inductive learning, including noise, missing values, small samples, inter-attribute dependency, and irrelevant or partially relevant attributes, all of which are characteristics of real- world induction tasks. The theoretical framework is based upon an uncertainty calculus, Dempster-Shafer theory which allows an explicit representation of complete or partial lack of knowledge. This explicit representation is used to quantify and discount the effects of unreliable probability estimates due to noise and small samples, and to account for inter-attribute dependency and irrelevant or partially relevant attributes. Based on this methodology, a learning system, called IUR (Induction of Uncertain Rules) that uses only the first-order correlation information, is developed and experimentally demonstrated to outperform the major existing induction systems on many of the standard test sets. Future research includes extending IUR to use higher-order correlation information and integrating the Extended Bayesian Belief Function approach to other learning paradigms such as decision trees and neural networks."
Data Science, Classification, and Related Methods
Author: International Federation of Classification Societies. Conference
language: en
Publisher: Springer
Release Date: 1998-03
This volume contains selected papers covering a wide range of topics, including theoretical and methodological advances relating to data gathering, classification and clustering, exploratory and multivariate data analysis, and knowledge seeking and discovery. The result is a broad view of the state of the art, making this an essential work not only for data analysts, mathematicians, and statisticians, but also for researchers involved in data processing at all stages from data gathering to decision making.
Artificial Intelligence in Real Time Control 1994 (AIRTC'94)
Paperback. Artificial Intelligence is one of the new technologies that has contributed to the successful development and implementation of powerful and friendly control systems. These systems are more attractive to end-users shortening the gap between control theory applications. The IFAC Symposia on Artificial Intelligence in Real Time Control provides the forum to exchange ideas and results among the leading researchers and practitioners in the field. This publication brings together the papers presented at the latest in the series and provides a key evaluation of present and future developments of Artificial Intelligence in Real Time Control system technologies.