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214 results on '"Duivesteijn, Wouter"'

1. Exceptional Subitizing Patterns: Exploring Mathematical Abilities of Finnish Primary School Children with Piecewise Linear Regression

Author

Schouten, Rianne M., Duivesteijn, Wouter, Räsänen, Pekka, Paul, Jacob M., Pechenizkiy, Mykola, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bifet, Albert, editor, Krilavičius, Tomas, editor, Miliou, Ioanna, editor, and Nowaczyk, Slawomir, editor

Published
2024
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3. Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles

Author

Du, Xin, Ramamoorthy, Subramanian, Duivesteijn, Wouter, Tian, Jin, and Pechenizkiy, Mykola

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Local decision rules are commonly understood to be more explainable, due to the local nature of the patterns involved. With numerical optimization methods such as gradient boosting, ensembles of local decision rules can gain good predictive performance on data involving global structure. Meanwhile, machine learning models are being increasingly used to solve problems in high-stake domains including healthcare and finance. Here, there is an emerging consensus regarding the need for practitioners to understand whether and how those models could perform robustly in the deployment environments, in the presence of distributional shifts. Past research on local decision rules has focused mainly on maximizing discriminant patterns, without due consideration of robustness against distributional shifts. In order to fill this gap, we propose a new method to learn and ensemble local decision rules, that are robust both in the training and deployment environments. Specifically, we propose to leverage causal knowledge by regarding the distributional shifts in subpopulations and deployment environments as the results of interventions on the underlying system. We propose two regularization terms based on causal knowledge to search for optimal and stable rules. Experiments on both synthetic and benchmark datasets show that our method is effective and robust against distributional shifts in multiple environments.

Published
2021

4. Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring

Author

Hess, Sibylle, Duivesteijn, Wouter, and Mocanu, Decebal

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

We formally prove the connection between k-means clustering and the predictions of neural networks based on the softmax activation layer. In existing work, this connection has been analyzed empirically, but it has never before been mathematically derived. The softmax function partitions the transformed input space into cones, each of which encompasses a class. This is equivalent to putting a number of centroids in this transformed space at equal distance from the origin, and k-means clustering the data points by proximity to these centroids. Softmax only cares in which cone a data point falls, and not how far from the centroid it is within that cone. We formally prove that networks with a small Lipschitz modulus (which corresponds to a low susceptibility to adversarial attacks) map data points closer to the cluster centroids, which results in a mapping to a k-means-friendly space. To leverage this knowledge, we propose Centroid Based Tailoring as an alternative to the softmax function in the last layer of a neural network. The resulting Gauss network has similar predictive accuracy as traditional networks, but is less susceptible to one-pixel attacks; while the main contribution of this paper is theoretical in nature, the Gauss network contributes empirical auxiliary benefits.

Published
2020

5. ELBA: Exceptional Learning Behavior Analysis

Author

Du, Xin, Duivesteijn, Wouter, Klabbers, Martijn, and Pechenizkiy, Mykola

Abstract

Behavioral records collected through course assessments, peer assignments, and programming assignments in Massive Open Online Courses (MOOCs) provide multiple views about a student's study style. Study behavior is correlated with whether or not the student can get a certificate or drop out from a course. It is of predominant importance to identify the particular behavioral patterns and establish an accurate predictive model for the learning results, so that tutors can give well-focused assistance and guidance on specific students. However, the behavioral records of individuals are usually very sparse; behavioral records between individuals are inconsistent in time and skewed in contents. These remain big challenges for the state-of-the-art methods. In this paper, we engage the concept of subgroup as a trade-off to overcome the sparsity of individual behavioral records and inconsistency between individuals. We employ the framework of Exceptional Model Mining (EMM) to discover exceptional student behavior. Various model classes of EMM are applied on dropout rate analysis, correlation analysis between length of learning behavior sequence and course grades, and passing state prediction analysis. Qualitative and quantitative experimental results on real MOOCs datasets show that our method can discover significantly interesting learning behavioral patterns of students. [For the full proceedings, see ED593090.]

Published
2018

6. k is the Magic Number -- Inferring the Number of Clusters Through Nonparametric Concentration Inequalities

Author

Hess, Sibylle and Duivesteijn, Wouter

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Most convex and nonconvex clustering algorithms come with one crucial parameter: the $k$ in $k$-means. To this day, there is not one generally accepted way to accurately determine this parameter. Popular methods are simple yet theoretically unfounded, such as searching for an elbow in the curve of a given cost measure. In contrast, statistically founded methods often make strict assumptions over the data distribution or come with their own optimization scheme for the clustering objective. This limits either the set of applicable datasets or clustering algorithms. In this paper, we strive to determine the number of clusters by answering a simple question: given two clusters, is it likely that they jointly stem from a single distribution? To this end, we propose a bound on the probability that two clusters originate from the distribution of the unified cluster, specified only by the sample mean and variance. Our method is applicable as a simple wrapper to the result of any clustering method minimizing the objective of $k$-means, which includes Gaussian mixtures and Spectral Clustering. We focus in our experimental evaluation on an application for nonconvex clustering and demonstrate the suitability of our theoretical results. Our \textsc{SpecialK} clustering algorithm automatically determines the appropriate value for $k$, without requiring any data transformation or projection, and without assumptions on the data distribution. Additionally, it is capable to decide that the data consists of only a single cluster, which many existing algorithms cannot.

Published
2019
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7. The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering

Author

Hess, Sibylle, Duivesteijn, Wouter, Honysz, Philipp, and Morik, Katharina

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

When it comes to clustering nonconvex shapes, two paradigms are used to find the most suitable clustering: minimum cut and maximum density. The most popular algorithms incorporating these paradigms are Spectral Clustering and DBSCAN. Both paradigms have their pros and cons. While minimum cut clusterings are sensitive to noise, density-based clusterings have trouble handling clusters with varying densities. In this paper, we propose \textsc{SpectACl}: a method combining the advantages of both approaches, while solving the two mentioned drawbacks. Our method is easy to implement, such as spectral clustering, and theoretically founded to optimize a proposed density criterion of clusterings. Through experiments on synthetic and real-world data, we demonstrate that our approach provides robust and reliable clusterings.

Published
2019

8. Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data

Author

Du, Xin, Sun, Lei, Duivesteijn, Wouter, Nikolaev, Alexander, and Pechenizkiy, Mykola

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Learning causal effects from observational data greatly benefits a variety of domains such as health care, education and sociology. For instance, one could estimate the impact of a new drug on specific individuals to assist the clinic plan and improve the survival rate. In this paper, we focus on studying the problem of estimating Conditional Average Treatment Effect (CATE) from observational data. The challenges for this problem are two-fold: on the one hand, we have to derive a causal estimator to estimate the causal quantity from observational data, where there exists confounding bias; on the other hand, we have to deal with the identification of CATE when the distribution of covariates in treatment and control groups are imbalanced. To overcome these challenges, we propose a neural network framework called Adversarial Balancing-based representation learning for Causal Effect Inference (ABCEI), based on the recent advances in representation learning. To ensure the identification of CATE, ABCEI uses adversarial learning to balance the distributions of covariates in treatment and control groups in the latent representation space, without any assumption on the form of the treatment selection/assignment function. In addition, during the representation learning and balancing process, highly predictive information from the original covariate space might be lost. ABCEI can tackle this information loss problem by preserving useful information for predicting causal effects under the regularization of a mutual information estimator. The experimental results show that ABCEI is robust against treatment selection bias, and matches/outperforms the state-of-the-art approaches. Our experiments show promising results on several datasets, representing different health care domains among others.

Published
2019
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9. A Clustering-Inspired Quality Measure for Exceptional Preferences Mining—Design Choices and Consequences

Author

Verhaegh, Ruben Franciscus Adrianus, Kiezebrink, Jacco Johannes Egbert, Nusteling, Frank, Rio, Arnaud Wander André, Bendicsek, Márton Bendegúz, Duivesteijn, Wouter, Schouten, Rianne Margaretha, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pascal, Poncelet, editor, and Ienco, Dino, editor

Published
2022
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11. Efficient Subgroup Discovery Through Auto-Encoding

Author

van der Haar, Joost F., Nagelkerken, Sander C., Smit, Igor G., van Straaten, Kjell, Tack, Janneke A., Schouten, Rianne M., Duivesteijn, Wouter, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bouadi, Tassadit, editor, Fromont, Elisa, editor, and Hüllermeier, Eyke, editor

Published
2022
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12. Controversy Rules - Discovering Regions Where Classifiers (Dis-)Agree Exceptionally

Author

Zeev-Ben-Mordehai, Oren, Duivesteijn, Wouter, and Pechenizkiy, Mykola

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Finding regions for which there is higher controversy among different classifiers is insightful with regards to the domain and our models. Such evaluation can falsify assumptions, assert some, or also, bring to the attention unknown phenomena. The present work describes an algorithm, which is based on the Exceptional Model Mining framework, and enables that kind of investigations. We explore several public datasets and show the usefulness of this approach in classification tasks. We show in this paper a few interesting observations about those well explored datasets, some of which are general knowledge, and other that as far as we know, were not reported before., Comment: 10 pages

Published
2018

13. Subjectively Interesting Subgroup Discovery on Real-valued Targets

Author

Lijffijt, Jefrey, Kang, Bo, Duivesteijn, Wouter, Puolamäki, Kai, Oikarinen, Emilia, and De Bie, Tijl

Subjects
Statistics - Machine Learning, Computer Science - Information Theory
Abstract

Deriving insights from high-dimensional data is one of the core problems in data mining. The difficulty mainly stems from the fact that there are exponentially many variable combinations to potentially consider, and there are infinitely many if we consider weighted combinations, even for linear combinations. Hence, an obvious question is whether we can automate the search for interesting patterns and visualizations. In this paper, we consider the setting where a user wants to learn as efficiently as possible about real-valued attributes. For example, to understand the distribution of crime rates in different geographic areas in terms of other (numerical, ordinal and/or categorical) variables that describe the areas. We introduce a method to find subgroups in the data that are maximally informative (in the formal Information Theoretic sense) with respect to a single or set of real-valued target attributes. The subgroup descriptions are in terms of a succinct set of arbitrarily-typed other attributes. The approach is based on the Subjective Interestingness framework FORSIED to enable the use of prior knowledge when finding most informative non-redundant patterns, and hence the method also supports iterative data mining., Comment: 12 pages, 10 figures, 2 tables, conference submission

Published
2017
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15. k Is the Magic Number—Inferring the Number of Clusters Through Nonparametric Concentration Inequalities

Author

Hess, Sibylle, Duivesteijn, Wouter, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Brefeld, Ulf, editor, Fromont, Elisa, editor, Hotho, Andreas, editor, Knobbe, Arno, editor, Maathuis, Marloes, editor, and Robardet, Céline, editor

Published
2020
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16. DEvIANT: Discovering Significant Exceptional (Dis-)Agreement Within Groups

Author

Belfodil, Adnene, Duivesteijn, Wouter, Plantevit, Marc, Cazalens, Sylvie, Lamarre, Philippe, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Brefeld, Ulf, editor, Fromont, Elisa, editor, Hotho, Andreas, editor, Knobbe, Arno, editor, Maathuis, Marloes, editor, and Robardet, Céline, editor

Published
2020
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17. Predicting Remaining Useful Life with Similarity-Based Priors

Author

Soons, Youri, Dijkman, Remco, Jilderda, Maurice, Duivesteijn, Wouter, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Berthold, Michael R., editor, Feelders, Ad, editor, and Krempl, Georg, editor

Published
2020
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20. Introducing exceptional growth mining: Analyzing the impact of soil characteristics on on-farm crop growth and yield variability

Author

Mulders, Puck J.A.M., van den Heuvel, Edwin R., Reidsma, Pytrik, Duivesteijn, Wouter, Mulders, Puck J.A.M., van den Heuvel, Edwin R., Reidsma, Pytrik, and Duivesteijn, Wouter

Abstract

Sustainable intensification of agriculture requires understanding of the effect of soil characteristics and nutrient supply on crop growth. As farms are increasing in size by acquiring small fields from various farmers, the soil characteristics and nutrient supply might be very different from field to field, while at the same time specific soil properties might limit the nutrient uptake. As a result, there might be a large number of heterogeneous reasons why crop growth varies significantly. New data analysis techniques can help to explain variability in crop growth among fields. This paper introduces Exceptional Growth Mining (EGM) as a first contribution. EGM instantiates the data mining framework Exceptional Model Mining (EMM) such that subgroups of fields can be found that grow exceptionally in terms of three growth parameters (high/low maximum growth, steep/flat linear growth and early/late midpoint of maximum growth). As second contribution, we apply EGM to a case study by analyzing the dataset of a potato farm in the south of the Netherlands. EGM consists of (i) estimating growth curves by applying nonlinear mixed models, (ii) investigating the correlation between the estimated growth parameters, and (iii) applying EMM on these growth curve parameters using a growth curve-specific quality measure. By applying EGM on the data of the potato farm, we obtain the following results: 1) the estimated growth curves represent the variability in potato tuber growth very well (R2 of 0.92), 2) the steepness of the growth curve has a strong correlation with the maximum growth and the midpoint of maximum growth, and the correlation between the midpoint of maximum growth and maximum growth is weak, 3) the subgroup analyses indicate that: high values of K correspond to high maxima; low values of K correspond to low maxima, steep growth curves’, and a late midpoint of halfway growth; Mg influences the midpoint of the growth curve; values of B are higher on dry s

Published
2024

21. ICIE 1.0: A Novel Tool for Interactive Contextual Interaction Explanations

Author

van der Zon, Simon B., Duivesteijn, Wouter, van Ipenburg, Werner, Veldsink, Jan, Pechenizkiy, Mykola, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Alzate, Carlos, editor, Monreale, Anna, editor, Bioglio, Livio, editor, Bitetta, Valerio, editor, Bordino, Ilaria, editor, Caldarelli, Guido, editor, Ferretti, Andrea, editor, Guidotti, Riccardo, editor, Gullo, Francesco, editor, Pascolutti, Stefano, editor, Pensa, Ruggero G., editor, Robardet, Celine, editor, and Squartini, Tiziano, editor

Published
2019
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25. Have It Both Ways—From A/B Testing to A&B Testing with Exceptional Model Mining

Author

Duivesteijn, Wouter, Farzami, Tara, Putman, Thijs, Peer, Evertjan, Weerts, Hilde J. P., Adegeest, Jasper N., Foks, Gerson, Pechenizkiy, Mykola, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Altun, Yasemin, editor, Das, Kamalika, editor, Mielikäinen, Taneli, editor, Malerba, Donato, editor, Stefanowski, Jerzy, editor, Read, Jesse, editor, Žitnik, Marinka, editor, Ceci, Michelangelo, editor, and Džeroski, Sašo, editor

Published
2017
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26. SCHEP — A Geometric Quality Measure for Regression Rule Sets, Gauging Ranking Consistency Throughout the Real-Valued Target Space

Author

Duivesteijn, Wouter, Meeng, Marvin, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Michaelis, Stefan, editor, Piatkowski, Nico, editor, and Stolpe, Marco, editor

Published
2016
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27. Exceptional Preferences Mining

Author

Rebelo de Sá, Cláudio, Duivesteijn, Wouter, Soares, Carlos, Knobbe, Arno, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Calders, Toon, editor, Ceci, Michelangelo, editor, and Malerba, Donato, editor

Published
2016
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28. Introducing exceptional growth mining—Analyzing the impact of soil characteristics on on-farm crop growth and yield variability.

Author

Mulders, Puck J. A. M., van den Heuvel, Edwin R., Reidsma, Pytrik, and Duivesteijn, Wouter

Subjects
CROP growth, CROP yields, SUSTAINABLE agriculture, POTATOES, AGRICULTURAL intensification, SOILS, IRON fertilizers, FARM size
Abstract

Sustainable intensification of agriculture requires understanding of the effect of soil characteristics and nutrient supply on crop growth. As farms are increasing in size by acquiring small fields from various farmers, the soil characteristics and nutrient supply might be very different from field to field, while at the same time specific soil properties might limit the nutrient uptake. As a result, there might be a large number of heterogeneous reasons why crop growth varies significantly. New data analysis techniques can help to explain variability in crop growth among fields. This paper introduces Exceptional Growth Mining (EGM) as a first contribution. EGM instantiates the data mining framework Exceptional Model Mining (EMM) such that subgroups of fields can be found that grow exceptionally in terms of three growth parameters (high/low maximum growth, steep/flat linear growth and early/late midpoint of maximum growth). As second contribution, we apply EGM to a case study by analyzing the dataset of a potato farm in the south of the Netherlands. EGM consists of (i) estimating growth curves by applying nonlinear mixed models, (ii) investigating the correlation between the estimated growth parameters, and (iii) applying EMM on these growth curve parameters using a growth curve-specific quality measure. By applying EGM on the data of the potato farm, we obtain the following results: 1) the estimated growth curves represent the variability in potato tuber growth very well (R2 of 0.92), 2) the steepness of the growth curve has a strong correlation with the maximum growth and the midpoint of maximum growth, and the correlation between the midpoint of maximum growth and maximum growth is weak, 3) the subgroup analyses indicate that: high values of K correspond to high maxima; low values of K correspond to low maxima, steep growth curves', and a late midpoint of halfway growth; Mg influences the midpoint of the growth curve; values of B are higher on dry soils with high tuber growth, while low values of B are found on wet soils with high tuber growth; high values of Zn, Mn, and Fe are found in subgroups with low tuber weight, probably related to the soil's low pH. In summary, this paper introduces EGM to obtain understanding in crop response to soil properties and nutrient supply. In addition, EGM provides a way to analyze only small parts of a large dataset, such that the impact of soil factors on growth can be analyzed on a more detailed level than existing methods. [ABSTRACT FROM AUTHOR]

Published
2024
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30. Understanding Where Your Classifier Does (Not) Work

Author

Duivesteijn, Wouter, Thaele, Julia, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Bifet, Albert, editor, May, Michael, editor, Zadrozny, Bianca, editor, Gavalda, Ricard, editor, Pedreschi, Dino, editor, Bonchi, Francesco, editor, Cardoso, Jaime, editor, and Spiliopoulou, Myra, editor

Published
2015
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33. RealKrimp — Finding Hyperintervals that Compress with MDL for Real-Valued Data

Author

Witteveen, Jouke, Duivesteijn, Wouter, Knobbe, Arno, Grünwald, Peter, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Blockeel, Hendrik, editor, van Leeuwen, Matthijs, editor, and Vinciotti, Veronica, editor

Published
2014
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35. Cost-Based Quality Measures in Subgroup Discovery

Author

Konijn, Rob M., Duivesteijn, Wouter, Meeng, Marvin, Knobbe, Arno, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Li, Jiuyong, editor, Cao, Longbing, editor, Wang, Can, editor, Tan, Kay Chen, editor, Liu, Bo, editor, Pei, Jian, editor, and Tseng, Vincent S., editor

Published
2013
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36. Discovering Local Subgroups, with an Application to Fraud Detection

Author

Konijn, Rob M., Duivesteijn, Wouter, Kowalczyk, Wojtek, Knobbe, Arno, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Pei, Jian, editor, Tseng, Vincent S., editor, Cao, Longbing, editor, Motoda, Hiroshi, editor, and Xu, Guandong, editor

Published
2013
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37. Multi-label LeGo — Enhancing Multi-label Classifiers with Local Patterns

Author

Duivesteijn, Wouter, Loza Mencía, Eneldo, Fürnkranz, Johannes, Knobbe, Arno, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Hollmén, Jaakko, editor, Klawonn, Frank, editor, and Tucker, Allan, editor

Published
2012
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38. Multilayer Perceptron for Label Ranking

Author

Ribeiro, Geraldina, Duivesteijn, Wouter, Soares, Carlos, Knobbe, Arno, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Villa, Alessandro E. P., editor, Duch, Włodzisław, editor, Érdi, Péter, editor, Masulli, Francesco, editor, and Palm, Günther, editor

Published
2012
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46. Exceptional Model Mining for Repeated Cross-Sectional Data (EMM-RCS) --- Supplementary Material

Author

Schouten, Rianne Margaretha, Duivesteijn, Wouter, and Pechenizkiy, Mykola

Abstract

Repeated Cross-Sectional (RCS) data measures a phenomenon by repeatedly sampling new cases from a population at successive measurement moments. It allows for analyzing societal trends without the need to follow people. To gain a deeper understanding of these trends, in [14] we proposed EMM-RCS, an Exceptional Model Mining instance designed to find subgroups displaying exceptional trend behavior in RCS data. We built quality measures on the standard error, finding various types of exceptionalities within trends (exceptional flattening, slope, deviation from the norm). Additionally, EMM-RCS can handle practical RCS data problems, including uneven spacing of measurements over time, fluctuating sample sizes, and missing data. Here, we supplement the publication [14] with the following information. First, we explain our choice of search algorithm, parameter settings, anti-redundancy and validation techniques, and give a notion of the computational complexity of our method. Secondly, we provide extra synthetic data results. Thirdly, we discuss how threshold values can be evaluated using the Distribution of False Discoveries (DFD) [5]. Fourthly, we provide experimental results for an additional real-world dataset: the Eurobarometer. Finally, we provide further explanation and a demonstration of the working of a refinement strategy for incomplete descriptive attributes as introduced in [14, Definition 4.3].

Published
2022
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