%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\sessionHeader{Tue3A: Dimensionality Reduction, Feature Selection and Extraction
}{Katharina Morik}{Chem, LT1}
\timeTitlePeople{16:10--16:35}{Embedding Monte Carlo Search of Features in Tree-Based Ensemble Methods}
{Francis Maes, Pierre Geurts and Louis Wehenkel} 

\abst{Feature generation is the problem of automatically constructing good features for a given target learning problem. While most feature generation algorithms belong either to the \textit{filter} or to the \textit{wrapper} approach, this paper focuses on \textit{embedded} feature generation.
We propose a general scheme to embed feature generation in a wide
range of tree-based learning algorithms, including single decision
trees, random forests and tree boosting. It is based on the
formalization of feature construction as a sequential decision making
problem addressed by a tractable Monte Carlo search algorithm coupled
with node splitting. This leads to fast algorithms that are applicable
to large-scale problems. We empirically analyze the performances of
these tree-based learners combined or not with the feature generation
capability on several standard datasets.  }


\timeTitlePeople{16:35--17:00}{Hypergraph Spectra for Semi-supervised Feature Selection}
{Zhihong Zhang, Edwin R.\ Hancock and Xiao Bai}

\abst{In many data analysis tasks, one is often confronted with the
problem of selecting features from very high dimensional data. Most
existing feature selection methods focus on ranking individual
features based on a utility criterion, and select the optimal feature
set in a greedy manner. However, the feature combinations found in
this way do not give optimal classification performance, since they
neglect the correlations among features. While the labeled data
required by supervised feature selection can be scarce, there is
usually no shortage of unlabeled data. In this paper, we propose a
novel hypergraph based semi-supervised feature selection algorithm to
select relevant features using both labeled and unlabeled data.  There
are two main contributions in this paper. The first is that by
incorporating multidimensional interaction information (MII) for
higher order similarities measure, we establish a novel hypergraph
framework which is used for characterizing the multiple relationships
within a set of samples. Thus, the structural information latent in
the data can be more effectively modeled.  Secondly, we derive a
hypergraph subspace learning view of feature selection which casting
the feature discriminant analysis into a regression framework that
considers the correlations among features. As a result, we can
evaluate joint feature combinations, rather than being confined to
consider them individually. Experimental results demonstrate the
effectiveness of our feature selection method on a number of standard
face data-sets.}

\timeTitlePeople{17:00--17:25}{Learning Neighborhoods for Metric Learning}
{Jun Wang, Adam Woznica and Alexandros Kalousis}

\abst{Metric learning methods have been shown to perform well on different learning tasks. Many of 
them rely on target neighborhood relationships that are computed in
the original feature space and remain fixed throughout learning.  As a
result, the learned metric reflects the original neighborhood
relations.  We propose a novel formulation of the metric learning
problem in which, in addition to the metric, the target neighborhood
relations are also learned in a two-step iterative approach. The new
formulation can be seen as a generalization of many existing metric
learning methods.  The formulation includes a target neighbor
assignment rule that assigns different numbers of neighbors to
instances according to their quality; `high quality' instances get
more neighbors.  We experiment with two of its instantiations that
correspond to the metric learning algorithms LMNN and MCML and compare
it to other metric learning methods on a number of datasets. The
experimental results show state-of-the-art performance and provide
evidence that learning the neighborhood relations does improve
predictive performance.}


\timeTitlePeople{17:25--17:50}{PCA, Eigenvector Localization and Clustering for Side-Channel Attacks on
Cryptographic Hardware Devices}
{Dimitrios Mavroeidis, Lejla Batina, Twan van Laarhoven and Elena Marchiori} 

\abst{Spectral methods, ranging from traditional Principal Components Analysis
to modern Laplacian matrix factorization, have proven to be a valuable
tool for a wide range of diverse data mining applications. Commonly
these methods are stated as optimization problems and employ the
extremal (maximal or minimal) eigenvectors of a certain input matrix
for deriving the appropriate statistical inferences. Interestingly,
recent studies have questioned this ``modus operandi" and revealed
that useful information may also be present within low-order
eigenvectors whose mass is concentrated (localized) in a small part of
their indexes. An application context where localized low-order
eigenvectors have been successfully employed is ``Differential Power
Analysis" (DPA).  DPA is a well studied side-channel attack on
cryptographic hardware devices (such as smart cards) that employs
statistical analysis of the device's power consumption in order to
retrieve the secret key of the cryptographic algorithm.  In this work
we propose a data mining (clustering) formulation of the DPA process
and also provide a theoretical model that justifies and explains the
utility of low-order eigenvectors.  In our data mining formulation, we
consider that the key-relevant information is modelled as a
``low-signal" pattern that is embedded in a ``high-noise" dataset. In
this respect our results generalize beyond DPA and are applicable to
analogous low-signal, hidden pattern problems.  The experimental
results using power trace measurements from a programmable smart card,
verify our approach empirically. }

\sessionend


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\sessionHeader{Tue3B: Multi-Relational Mining and Learning}{Pauli Miettinen}{Chem, LT2}
\timeTitlePeople{16:10--16:35}{Author Name Disambiguation Using a New Categorical Distribution Similarity}
{Shaohua Li, Gao Cong and Chunyan Miao } 

\vspace{-2mm}
\abst{Author name ambiguity has been a long-standing problem which impairs the accuracy of publication retrieval and bibliometric methods. 
Most of the existing disambiguation methods are built on similarity
measures, e.g., ``Jaccard Coefficient'', between two sets of papers to
be disambiguated, each set represented by a set of categorical
features, e.g., coauthors and published venues\footnote{Venues here
refer to the journal or conference, such as \emph{J. ACM}
or \emph{SIGIR}.}. Such measures perform bad when the two sets are
small, which is typical in Author Name Disambiguation. In this paper,
we propose a novel categorical set similarity measure. We model an
author's preference, e.g., to venues, using a categorical
distribution, and derive a likelihood ratio to estimate the likelihood
that the two sets are drawn from the same distribution. This
likelihood ratio is used as the similarity measure to decide whether
two sets belong to the same author. This measure is mathematically
principled and verified to perform well even when the cardinalities of
the two compared sets are small.  Additionally, we propose a new
method to estimate the number of distinct authors for a given name
based on the name statistics extracted from a digital
library. Experiment shows that our method significantly outperforms a
baseline method, a widely used benchmark method, and a real system.}

\timeTitlePeople{16:35--17:00}{Lifted Online Training of Relational Models with Stochastic Gradient Methods}
{Babak Ahmadi, Kristian Kersting and Sriraam Natarajan} 

\vspace{-2mm}
\abst{Lifted inference approaches have rendered
large, previously intractable probabilistic inference problems quickly
solvable by employing symmetries to handle whole sets of
indistinguishable random variables.  Still, in many if not most
situations training relational models will not benefit from lifting:
symmetries within models easily break since variables become
correlated by virtue of depending asymmetrically on evidence.  An
appealing idea for such situations is to train and recombine local
models. This breaks long-range dependencies and allows to exploit
lifting within and across the local training tasks. Moreover, it
naturally paves the way for online training for relational
models. Specifically, we develop the first lifted stochastic gradient
optimization method with gain vector adaptation, which processes each
lifted piece one after the other.  On several datasets, the resulting
optimizer converges to the same quality solution over an order of
magnitude faster, simply because unlike batch training it starts
optimizing long before having seen the entire mega-example even once.}


\timeTitlePeople{17:00--17:25}
{Scalable Relation Prediction Exploiting Both Intrarelational Correlation and Contextual\break Information}
{Xueyan Jiang, Volker Tresp, Yi Huang, Maximilian Nickel and Hans-Peter Kriegel}

\vspace{-2mm}
\abst{We consider the problem of predicting instantiated binary relations in a multi-relational setting and exploit both
intrarelational correlations and contextual information. For the
modular combination we discuss simple heuristics, additive models and
an approach that can be motivated from a hierarchical Bayesian
perspective. In the concrete examples we consider models that exploit
contextual information both from the database and from contextual
unstructured information, e.g., information extracted from textual
documents describing the involved entities.  By using low-rank
approximations in the context models, the models perform latent
semantic analyses and can generalize across specific terms, i.e., the
model might use similar latent representations for semantically
related terms.  All the approaches we are considering have unique
solutions.  They can exploit sparse matrix algebra and are thus highly
scalable and can easily be generalized to new entities. We evaluate
the effectiveness of nonlinear interaction terms and reduce the number
of terms by applying feature selection.  For the optimization of the
context model we use an alternating least squares approach. We
experimentally analyze scalability.  We validate our approach using
two synthetic data sets and using two data sets derived from the
Linked Open Data (LOD) cloud.}


\timeTitlePeople{17:25--17:50}{Relational Differential Prediction}
{Houssam Nassif, V\'{\i}tor {Santos Costa}, Elizabeth S.\ Burnside and David Page} 

\vspace{-2mm}
\abst{A typical classification problem involves building a model to
  correctly segregate instances of two or more classes. Such a model
  exhibits differential prediction with respect to given data subsets
  when its performance is significantly different over these
  subsets. Driven by a mammography application, we aim at learning
  rules that predict breast cancer stage while maximizing differential
  prediction over age-stratified data. In this work, we present the
  first multi-relational differential prediction (aka uplift modeling)
  system, and propose three different approaches to learn differential
  predictive rules within the Inductive Logic Programming framework.
  We first test and validate our methods on synthetic data, then apply
  them on a mammography dataset for breast cancer stage differential
  prediction rule discovery.  We mine a novel rule linking
  calcification to \emph{in situ} breast cancer in older women.}

\sessionend


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\sessionHeader{Tue3C: Semi-Supervised and Transductive Learning}{Florence d'Alch\'{e}}{Chem, LT3}
\timeTitlePeople{16:10--16:35}{Bidirectional Semi-supervised Learning with Graphs}{Tomoharu Iwata and Kevin Duh} 

\abst{We present a machine learning task, which we call bidirectional semi-supervised learning, where label-only samples are given as well as labeled and unlabeled samples. A label-only sample contains the label information of the sample but not the feature information. Then, we propose a simple and effective graph-based method for bidirectional semi-supervised learning in multi-label classification. The proposed method assumes that correlated classes are likely to have the same labels among the similar samples. First, we construct a graph that represents similarities between samples using labeled and unlabeled samples in the same way with graph-based semi-supervised methods. Second, we construct another graph using labeled and label-only samples by connecting classes that are likely to co-occur, which represents correlations between classes. Then, we estimate labels of unlabeled samples by propagating labels over these two graphs. We can find a closed-form global solution for the label propagation by using matrix algebra. We demonstrate the effectiveness of the proposed method over supervised and semi-supervised learning methods with experiments using synthetic and multi-label text data sets.}


\timeTitlePeople{16:35--17:00}{Graph-Based Transduction with Confidence}{Matan Orbach and Koby Crammer} 

\abst{We present a new multi-class graph-based transduction algorithm.
Examples are associated with vertices in an undirected weighted graph
and edge weights correspond to a similarity measure between examples.
Typical algorithms in such a setting perform label propagation between
neighbours, ignoring the quality, or estimated quality, in the
labeling of various nodes.  We introduce an additional quantity of
confidence in label assignments, and learn them jointly with the
weights, while using them to dynamically tune the influence of each
vertex on its neighbours.  We cast learning as a convex optimization
problem, and derive an efficient iterative algorithm for solving it.
Empirical evaluations on seven NLP data sets demonstrate our algorithm
improves over other state-of-the-art graph-based transduction
algorithms.}


\timeTitlePeople{17:00--17:25}{Maximum Consistency Preferential Random Walks}{Deguang Kong and Chris Ding} 

\abst{Random walk plays a significant role in computer science. The popular
PageRank algorithm uses random walk. Personalized
random walks force random walk to ``personalized views'' of
the graph according to users' preferences. In this paper, we show the close relations between different preferential random walks and label propagation methods
used in semi-supervised learning.
We further present a maximum consistency algorithm on these preferential random walk/label propagation methods to ensure maximum consistency from labeled data to unlabeled data.
Extensive experimental results on 9 datasets provide performance comparisons of different preferential random walks/label propagation methods. They also indicate that
the proposed maximum consistency algorithm clearly improves the
classification accuracy over existing methods.}


\timeTitlePeople{17:25--17:50}{Semi-supervised Multi-label Classification: A Simultaneous Large-Margin, Subspace\hfill\break Learning Approach}
{Yuhong Guo and Dale Schuurmans} 

\abst{Labeled data is often sparse in common learning scenarios,
either because it is too time consuming or too expensive to obtain,
while unlabeled data is almost always plentiful.
This asymmetry is exacerbated in \emph{multi-label} learning,
where the labeling process is more complex than in the single label case.
Although it is important to consider \emph{semi-supervised} methods for 
multi-label learning, as it is in other learning scenarios,
surprisingly, few proposals have been investigated for this particular problem.
In this paper, we present a new semi-supervised multi-label learning method
that combines large-margin multi-label classification with
unsupervised subspace learning. 
We propose an algorithm that learns a subspace representation of the labeled 
and unlabeled inputs, while simultaneously training a supervised large-margin 
multi-label classifier on the labeled portion.
Although joint training of these two interacting components might appear 
intractable, we exploit recent developments in induced matrix norm optimization
to show that these two problems can be solved jointly, globally and efficiently.
In particular, we develop an efficient training procedure
based on subgradient search and a simple coordinate descent strategy.
An experimental evaluation demonstrates that
semi-supervised subspace learning can improve the performance of
corresponding supervised multi-label learning methods.}

\sessionend


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\sessionHeader{Tue3D: Demo Spotlights}{Bettina Berendt and Myra Spiliopoulou}{Chem, LT4}
\timeTitlePeople{16:10--16:20}{VIKAMINE -- Open-Source Subgroup Discovery, Pattern Mining, and Analytics}
{Martin Atzmueller and Florian Lemmerich}

\abst{This paper presents an overview on 
the \textsc{VIKAMINE}\footnote{\url{http://www.vikamine.org}} system
for subgroup discovery, pattern mining and analytics.  As of \VIKAMINE
version 2, it is implemented as rich-client platform (RCP)
application, based on the
Eclipse\footnote{\url{http://www.eclipse.org}} framework. This
provides for a highly-configurable environment, and allows modular
extensions using plugins.  We present the system, briefly discuss
exemplary plugins, and provide a sketch of successful applications.}


\timeTitlePeople{16:20--16:30}{Association Rule Mining Following the Web Search Paradigm}
{Radek \v{S}krabal, Milan \v{S}im\r{u}nek, Stanislav Voj\'{\i}\v{r}, Andrej Hazucha, Tom\'{a}\v{s} Marek, David Chud\'{a}n and Tom\'{a}\v{s} Kliegr} 

\abst{I:ZI Miner (\url{sewebar.vse.cz/izi-miner}) is an  association rule mining  system with a user interface resembling a  search engine. It brings to the web  the notion of interactive pattern mining introduced by the MIME framework  at ECML'11 and KDD'11. In comparison with MIME, I:ZI Miner discovers multi-valued attributes, supports the full range of logical connectives and 19 interest measures. A relevance feedback module is used to filter the rules based on previous user  interactions. }

\continueSessionOnNewPage{Tue3D: Demo Spotlights}{Bettina Berendt and Myra Spiliopoulou}{LT4}
\timeTitlePeople{16:30--16:40}{\textit{OutRules}: A Framework for Outlier Descriptions in Multiple Context Spaces}
{Emmanuel M\"{u}ller, Fabian Keller, Sebastian Blanc and Klemens B\"{o}hm} 

\abst{Analyzing exceptional objects is an important mining task. It includes the identification of outliers but also the description of outlier properties in contrast to regular objects. However, existing \textit{detection} approaches miss to provide important \textit{descriptions} that allow human understanding of outlier reasons. In this work we present \textit{OutRules}, a framework for outlier descriptions that enable an easy understanding of multiple outlier reasons in different contexts. We introduce outlier rules as a novel outlier description model. A rule illustrates the deviation of an outlier in contrast to its context that is considered to be normal. Our framework highlights the practical use of outlier rules and provides the basis for future development of outlier description models.}


\timeTitlePeople{16:40--16:50}{Knowledge Discovery through Symbolic Regression with HeuristicLab}
{Gabriel Kronberger, Stefan Wagner, Michael Kommenda, Andreas Beham, Andreas Scheibenpflug and Michael Affenzeller} 

\abst{This contribution describes how symbolic regression can be used for knowledge discovery with the open-source software HeuristicLab. HeuristicLab includes a large set of algorithms and problems for combinatorial optimization and for regression and classification, including symbolic regression with genetic programming. It provides a rich GUI to analyze and compare algorithms and identified models. This contribution mainly focuses on specific aspects of symbolic regression that are unique to HeuristicLab, in particular, the identification of relevant variables and model simplification. }


\timeTitlePeople{16:50--17:00}{An Aspect-Lexicon Creation and Evaluation Tool for Sentiment Analysis Researchers}
{Mus'ab Husaini, Ahmet Ko\c{c}yi\u{g}it, Dilek Tapucu, Berrin Yanikoglu and Y\"{u}cel Sayg{\i}n} 

\abst{In this demo paper, we present SARE, a modular and extendable semi-automatic system that
\begin{inparaenum}[\itshape 1\upshape)]
\item assists researchers in building gold-standard lexicons and evaluating their lexicon extraction algorithms; and
\item provides a general and extendable sentiment analysis environment to help researchers analyze the behavior and errors of a core sentiment analysis engine using a particular lexicon.
\end{inparaenum}}


\timeTitlePeople{17:00--17:10}{ASV Monitor: Creating Comparability of Machine Learning Methods for Content Analysis}
{Andreas Niekler, Patrick J\"{a}hnichen and Gerhard Heyer} 

\abst{In this demonstration paper we present an application to compare and evaluate
machine learning methods used for natural language processing within a
content analysis framework. Our aim is to provide an example set of
possible machine learning results for different inputs to increase the
acceptance of using machine learning in settings that originally rely
on manual treatment. We will demonstrate the possibility to compare
machine learning algorithms regarding the outcome of the implemented
approaches. The application allows the user to evaluate the benefit of
using machine learning algorithms for content analysis by a visual
comparison of their results.}


\timeTitlePeople{17:10--17:20}{TopicExplorer: Exploring Document Collections with Topic Models}
{Alexander Hinneburg, Rico Preiss and Ren\'{e} Schr\"{o}der} 

\abst{The demo presents a prototype -- called \TopicExplorer -- that combines topic modeling, key word search and visualization techniques to explore a large collection of Wikipedia documents.
Topics derived by Latent Dirichlet Allocation are presented by top
words.  In addition, topics are accompanied by image thumbnails
extracted from related Wikipedia documents to aid sense making of
derived topics during browsing.  Topics are shown in a linear order
such that similar topics are close.  Topics are mapped to color using
that order.  The auto-completion of search terms suggests words
together with their color coded topics, which allows to explore the
relation between search terms and topics.  Retrieved documents are
shown with color coded topics as well.  Relevant documents and topics
found during browsing can be put onto a shortlist.  The tool can
recommend further documents with respect to the average topic mixture
of the shortlist.}


\timeTitlePeople{17:20--17:30}{Extracting Trajectories through an Efficient and Unifying Spatio-temporal Pattern Mining System}
{Phan Nhat Hai, Dino Ienco, Pascal Poncelet and Maguelonne Teisseire}

\abst{Recent improvements in positioning technology has led to a much wider availability of massive moving object data. A crucial task is to find the moving objects that travel together. Usually, these object sets are called spatio-temporal patterns. Analyzing such data has been applied in many real world applications, e.g., in ecological study, vehicle control, mobile communication management, etc. However, few tools are available for flexible and scalable analysis of massive scale moving objects. Additionally, there is no framework devoted to efficiently manage multiple kinds of patterns at the same time. Motivated by this issue, we propose a framework, named {\sc GeT\_Move}, which is designed to extract and manage different kinds of spatio-temporal patterns concurrently. A user-friendly interface is provided to facilitate interactive exploration of mining results. Since {\sc GeT\_Move} is tested on many kinds of real data sets, it will benefit users to carry out versatile analysis on these kinds of data by exhibiting different kinds of patterns efficiently.}


\timeTitlePeople{17:30--17:40}{Scientific Workflow Management with ADAMS}
{Peter Reutemann and Joaquin Vanschoren} 

\abst{We demonstrate the Advanced Data mining And Machine learning System (ADAMS), a novel workflow engine designed for rapid prototyping and maintenance of complex knowledge workflows. ADAMS does not require the user to manually connect inputs to outputs on a large canvas. It uses a compact workflow representation, \emph{control operators}, and a simple interface between operators, allowing them to be auto-connected. It contains an extensive library of operators for various types of analysis, and a convenient plug-in architecture to easily add new ones.}


\timeTitlePeople{17:40--17:50}{ClowdFlows: A Cloud Based Scientific Workflow Platform}
{Janez Kranjc, Vid Podpe\v{c}an and Nada Lavra\v{c}} 

\abst{This paper presents an open cloud based platform for composition, execution, and sharing of interactive data mining workflows. It is based on the principles of service-oriented knowledge discovery, and features interactive scientific workflows. In contrast to comparable data mining platforms, our platform runs in all major Web browsers and platforms, including mobile devices. In terms of crowdsourcing, ClowdFlows provides researchers with an easy way to expose and share their work and results, as only an Internet connection and a Web browser are required to access the workflows from anywhere. Practitioners can use ClowdFlows to seamlessly integrate and join different implementations of algorithms, tools and Web services into a coherent workflow that can be executed in a cloud based application. ClowdFlows is also easily extensible during run-time by importing Web services and using them as new workflow components.
}


\sessionend
