Intelligent Information Systems 2001

1. Front Matter

   Pages I-XII

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2. Rule and Decision Tree Learning

   1. Identification of Rules for Brain Metastases and Survival Time Prediction for Small Cell Lung Cancer Patients

      • Kwei Aryeetey, Wojciech Ziarko, Patricia Tai, Casely Ago

      Pages 1-12

   2. The Development of the AQ20 Learning System and Initial Experiments

      • Guido Cervone, Liviu Panait, Ryszard Michalski

      Pages 13-29

   3. Discovering Multi-head Attributional Rules in Large Databases

      • Cezary Głowiński, Ryszard S. Michalski

      Pages 31-41

   4. Melanoma Prediction Using k-Nearest Neighbor and LEM2 Algorithms

      • Jerzy W. Grzymala-Busse, Zdzislaw S. Hippe

      Pages 43-55

   5. Technologies for Sound Database Indexing: Musical Instrument Classification Methods

      • Alicja A. Wieczorkowska

      Pages 57-66

3. Unsupervised Learning

   1. Significance Testing in the Grade Correspondence Analysis

      • Alicja Ciok

      Pages 67-74

   2. Ensembles of Similarity-Based Models

      • Włodzisław Duch, Karol Grudziński

      Pages 75-85

   3. Regression — Yet Another Clustering Method

      • Piotr Gawrysiak, Michal Okoniewski, Henryk Rybinski

      Pages 87-95

   4. Taxonomy Building: Cases or Attributes ?

      • Mieczyslaw A. Klopotek

      Pages 97-110

   5. Using Grade Correspondence Analysis to Merge Populations and Detect Latent Orders

      • Wiesław Szczesny, Olaf Matyja

      Pages 111-120

   6. Mining the Preference Relations and Preference Graphs

      • Jan M. Żytkow, Daniel Arredondo

      Pages 121-131

4. Biologically Motivated Methods

   1. Multi-Dimensional Gaussian and Cauchy Mutations

      • Andrzej Obuchowicz

      Pages 133-142

   2. Statistical Genetic Algorithms

      • Andrzej Ossowski, Anna Święcicka

      Pages 143-154

   3. Cellular Automata Approach to Scheduling Problem in Case of Modifications of a Program Graph

      • Anna Święcicka, Franciszek Seredyński, Mariusz Jażdżyk

      Pages 155-166

   4. Multimodal Optimization with Artificial Immune Systems

      • Sławomir T. Wierzchoń

      Pages 167-178

5. Learning and Reasoning Under Uncertainty

   1. Bayesian Network Mining System

      • Mieczysław A. Kłopotek, Sławomir T. Wierzchoń, Maciej Michalewicz, Marek Bednarczyk, Wiesław Pawłowski, Andrzej Wąsowski

      Pages 179-193

   2. Intelligent Techniques in Stock Analysis

      • Halina Kwaśnicka, Marcin Ciosmak

      Pages 195-208

   3. Models of Multiple Response Independence

      • Andrzej Matuszewski, Krzysztof Trojanowski

      Pages 209-220

   4. The Role of Support and Confidence in Collaborative Query Answering

      • Zbigniew W. Ras

      Pages 221-226

This volume contains articles accepted for presentation during The Intelli­ gent Information Systems Symposium IIS'2001 which was held in Zakopane, Poland, on June 18-22, 200l. This is tenth, in the order, symposium orga­ nized by the Institute of Computer Science of Polish Academy of Sciences and devoted to new trends in (broadly understood) Artificial Intelligence. The idea of organizing such meetings dates back to 1992. Our main in­ tention guided the first, rather small-audience, workshop in the series was to resume the results gained in Polish scientific centers as well as contrast them with the esea r rch performed by Polish scientists working at the uni­ versities in Europe and USA. This idea proved to be attractive enough that we decided to continue such meetings. As the years went by, the workshops has transformed into regular symposia devoted to such fields like Machine Learning, Knowledge Discovery, Natural Language Processing, Knowledge Based Systems and Reasoning, and Soft Computing (i.e. Fuzzy and Rough Sets, Bayesian Networks, Neural Networks and Evolutionary Algorithms). At present, about 50 papers prepared by researches from Poland and other countries are usually presented. Besides, for several years now, the symposia are accompanied by a number of tutorials, given by the outstanding scientists in their domain.

• Bayesian network
• Data Mining
• Evolution
• Fuzzy
• Intelligent Information Systems
• Machine Learning
• Soft Computing
• evolutionary algorithm
• information system
• knowledge
• knowledge base
• knowledge discovery
• learning
• optimization
• verification

• Polish Academy of Sciences, Warsaw, Poland

  Mieczysław A. Kłopotek, Maciej Michalewicz, Sławomir T. Wierzchoń

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