Advances in Intelligent Data Analysis VII

1. Front Matter

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2. Statistical Data Analysis

   1. Compact and Understandable Descriptions of Mixtures of Bernoulli Distributions

      • Jaakko Hollmén, Jarkko Tikka

      Pages 1-12

   2. Multiplicative Updates for L ₁–Regularized Linear and Logistic Regression

      • Fei Sha, Y. Albert Park, Lawrence K. Saul

      Pages 13-24

   3. Learning to Align: A Statistical Approach

      • Elisa Ricci, Tijl de Bie, Nello Cristianini

      Pages 25-36

   4. Transductive Reliability Estimation for Kernel Based Classifiers

      • Dimitris Tzikas, Matjaz Kukar, Aristidis Likas

      Pages 37-47

3. Bayesian Approaches

   1. Parameter Learning for Bayesian Networks with Strict Qualitative Influences

      • Ad Feelders, Robert van Straalen

      Pages 48-58

   2. Tree Augmented Naive Bayes for Regression Using Mixtures of Truncated Exponentials: Application to Higher Education Management

      • Antonio Fernández, María Morales, Antonio Salmerón

      Pages 59-69

4. Clustering Methods

   1. DENCLUE 2.0: Fast Clustering Based on Kernel Density Estimation

      • Alexander Hinneburg, Hans-Henning Gabriel

      Pages 70-80

   2. Visualising the Cluster Structure of Data Streams

      • Dimitris K. Tasoulis, Gordon Ross, Niall M. Adams

      Pages 81-92

   3. Relational Topographic Maps

      • Alexander Hasenfuss, Barbara Hammer

      Pages 93-105

5. Ensemble Learning

   1. Incremental Learning with Multiple Classifier Systems Using Correction Filters for Classification

      • José del Campo-Ávila, Gonzalo Ramos-Jiménez, Rafael Morales-Bueno

      Pages 106-117

   2. Combining Bagging and Random Subspaces to Create Better Ensembles

      • Panče Panov, Sašo Džeroski

      Pages 118-129

   3. Two Bagging Algorithms with Coupled Learners to Encourage Diversity

      • Carlos Valle, Ricardo Ñanculef, Héctor Allende, Claudio Moraga

      Pages 130-139

6. Ranking

   1. Relational Algebra for Ranked Tables with Similarities: Properties and Implementation

      • Radim Belohlavek, Stanislav Opichal, Vilem Vychodil

      Pages 140-151

   2. A New Way to Aggregate Preferences: Application to Eurovision Song Contests

      • Jérémy Besson, Céline Robardet

      Pages 152-162

7. Trees

   1. Conditional Classification Trees Using Instrumental Variables

      • Valerio A. Tutore, Roberta Siciliano, Massimo Aria

      Pages 163-173

   2. Robust Tree-Based Incremental Imputation Method for Data Fusion

      • Antonio D’Ambrosio, Massimo Aria, Roberta Siciliano

      Pages 174-183

8. Sequence/ Time Series Analysis

   1. Making Time: Pseudo Time-Series for the Temporal Analysis of Cross Section Data

      • Emma Peeling, Allan Tucker

      Pages 184-194

   2. Recurrent Predictive Models for Sequence Segmentation

      • Saara Hyvönen, Aristides Gionis, Heikki Mannila

      Pages 195-206

   3. Sequence Classification Using Statistical Pattern Recognition

      • José Antonio Iglesias, Agapito Ledezma, Araceli Sanchis

      Pages 207-218

Weareproudtopresenttheproceedingsoftheseventhbiennialconferenceinthe Intelligent Data Analysis series. The conference took place in Ljubljana, Slo- nia, September 6-8, 2007. IDA continues to expand its scope, quality and size. It started as a small side-symposium as part of a larger conference in 1995 in Baden-Baden(Germany).It quickly attractedmoreinterest in both submissions and attendance as it moved to London (1997) and then Amsterdam (1999). The next three meetings were held in Lisbon (2001), Berlin (2003) and then Madrid in 2005. The improving quality of the submissions has enabled the organizers to assemble programs of ever-increasing consistency and quality. This year we madea rigorousselectionof33papersoutofalmost100submissions.Theresu- ing oral presentations were then scheduled in a single-track, two-and-a-half-day conference program, summarized in the book that you have before you. In accordance with the stated IDA goal of “bringing together researchers from diverse disciplines,” we believe we have achieved an excellent balance of presentationsfromthemoretheoretical–bothstatisticalandmachinelearning– to the more application-oriented areas that illustrate how these techniques can beusedinpractice.Forexample,theproceedingsincludepaperswiththeoretical contributions dealing with statistical approaches to sequence alignment as well as papers addressing practical problems in the areas of text classi?cation and medical data analysis. It is reassuring to see that IDA continues to bring such diverse areas together, thus helping to cross-fertilize these ?elds.

• Analysis
• classification
• cognition
• data analysis
• data mining
• learning
• machine learning
• modeling
• pattern recognition
• statistics
• visualization

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