Date: Wed, 16 Dec 1998 11:30:05 -0500
From: A. Famili Fazel.Famili@iit.nrc.ca
Subject: CFP: Pre- and Post-Processing in Machine Learning and Data Mining:
Web: http://www.iit.demokritos.gr/skel/eetn/acai99
:::::A WORKSHOP WITHIN::::
MACHINE LEARNING AND APPLICATIONS
Advanced Course on Artificial Intelligence 1999 (ACAI-99)
5-16 July 1999, Greece
(http://www.iit.demokritos.gr/skel/eetn/acai99)
This workshop addresses an important aspect related to the
Data Mining (DM) and Machine Learning (ML) in pre-processing and
analyzing real-world data.
First, the data which are to be processed by a DM
algorithm are usually noisy and often inconsistent. Many steps are
involved before the actual data analysis starts. Moreover, the
genuine logical ML systems do not easily allow processing of numerical
attributes as well as numerical (continuous) classes. Therefore, certain
procedures have to precede the actual data analysis process.
Second, a result of a genuine ML algorithm, such as a decision
tree or a set of decision rules, need not be perfect from the view of
custom or commercial applications. It is quite known that a concept
description as a result of an inductive process has to be usually
processed by a pre- or post-pruning procedure. Other post-processing
procedures include rule quality processing, rule filtering, rule
combination, or even knowledge integration. All these procedures provide a
kind of "symbolic filter" for noisy, imprecise, or "non-user-friendly"
knowledge derived by an inductive algorithm.
[edited GPS]
Important Dates (might be changed according to the ACAI-99 programming
committee):
Deadline for papers: 1-Mar-99
Acceptance: 15-Mar-99
Camera ready copy: 1-Apr-99
for full information see http://www.iit.demokritos.gr/skel/eetn/acai99
Copyright © 1999 KDnuggets