pwc-archive/papers-with-abstracts huggingface dataset:
Investigating the Detection of Adverse Drug Events in a UK General Practice Electronic Health-Care Database
Authors: Jenna Reps, Jan Feyereisl, Jonathan M. Garibaldi, Uwe Aickelin, Jack E. Gibson, Richard B. Hubbard
Date: 2013-07-03 00:00:00

Data-mining techniques have frequently been developed for Spontaneous
reporting databases. These techniques aim to find adverse drug events
accurately and efficiently. Spontaneous reporting databases are prone to
missing information, under reporting and incorrect entries. This often results
in a detection lag or prevents the detection of some adverse drug events. These
limitations do not occur in electronic health-care databases. In this paper,
existing methods developed for spontaneous reporting databases are implemented
on both a spontaneous reporting database and a general practice electronic
health-care database and compared. The results suggests that the application of
existing methods to the general practice database may help find signals that
have gone undetected when using the spontaneous reporting system database. In
addition the general practice database provides far more supplementary
information, that if incorporated in analysis could provide a wealth of
information for identifying adverse events more accurately.
A Comparison of Non-stationary, Type-2 and Dual Surface Fuzzy Control
Authors: Naisan Benatar, Uwe Aickelin, Jonathan M. Garibaldi
Date: 2013-07-03 00:00:00
Tasks: Vocal Bursts Type Prediction

Type-1 fuzzy logic has frequently been used in control systems. However this
method is sometimes shown to be too restrictive and unable to adapt in the
presence of uncertainty. In this paper we compare type-1 fuzzy control with
several other fuzzy approaches under a range of uncertain conditions. Interval
type-2 and non-stationary fuzzy controllers are compared, along with 'dual
surface' type-2 control, named due to utilising both the lower and upper values
produced from standard interval type-2 systems. We tune a type-1 controller,
then derive the membership functions and footprints of uncertainty from the
type-1 system and evaluate them using a simulated autonomous sailing problem
with varying amounts of environmental uncertainty. We show that while these
more sophisticated controllers can produce better performance than the type-1
controller, this is not guaranteed and that selection of Footprint of
Uncertainty (FOU) size has a large effect on this relative performance.
An Efficient Model Selection for Gaussian Mixture Model in a Bayesian Framework
Authors: Ji Won Yoon
Date: 2013-07-03 00:00:00
Tasks: Clustering, model, Model Selection

In order to cluster or partition data, we often use
Expectation-and-Maximization (EM) or Variational approximation with a Gaussian
Mixture Model (GMM), which is a parametric probability density function
represented as a weighted sum of $\hat{K}$ Gaussian component densities.
However, model selection to find underlying $\hat{K}$ is one of the key
concerns in GMM clustering, since we can obtain the desired clusters only when
$\hat{K}$ is known. In this paper, we propose a new model selection algorithm
to explore $\hat{K}$ in a Bayesian framework. The proposed algorithm builds the
density of the model order which any information criterions such as AIC and BIC
basically fail to reconstruct. In addition, this algorithm reconstructs the
density quickly as compared to the time-consuming Monte Carlo simulation.
Extending UML for Conceptual Modeling of Annotation of Medical Images
Authors: Mouhamed Gaith Ayadi, Riadh Bouslimi, Jalel Akaichi
Date: 2013-07-03 00:00:00
Tasks: Management, Specificity

Imaging has occupied a huge role in the management of patients, whether
hospitalized or not. Depending on the patients clinical problem, a variety of
imaging modalities were available for use. This gave birth of the annotation of
medical image process. The annotation is intended to image analysis and solve
the problem of semantic gap. The reason for image annotation is due to increase
in acquisition of images. Physicians and radiologists feel better while using
annotation techniques for faster remedy in surgery and medicine due to the
following reasons: giving details to the patients, searching the present and
past records from the larger databases, and giving solutions to them in a
faster and more accurate way. However, classical conceptual modeling does not
incorporate the specificity of medical domain specially the annotation of
medical image. The design phase is the most important activity in the
successful building of annotation process. For this reason, we focus in this
paper on presenting the conceptual modeling of the annotation of medical image
by defining a new profile using the StarUML extensibility mechanism.
Separation of cardiac and respiratory components from the electrical bio-impedance signal using PCA and fast ICA
Authors: Yar M. Mughal, A. Krivoshei, P. Annus
Date: 2013-07-03 00:00:00

This paper is an attempt to separate cardiac and respiratory signals from an
electrical bio-impedance (EBI) dataset. For this two well-known algorithms,
namely Principal Component Analysis (PCA) and Independent Component Analysis
(ICA), were used to accomplish the task. The ability of the PCA and the ICA
methods first reduces the dimension and attempt to separate the useful
components of the EBI, the cardiac and respiratory ones accordingly. It was
investigated with an assumption, that no motion artefacts are present. To carry
out this procedure the two channel complex EBI measurements were provided using
classical Kelvin type four electrode configurations for the each complex
channel. Thus four real signals were used as inputs for the PCA and fast ICA.
The results showed, that neither PCA nor ICA nor combination of them can not
accurately separate the components at least are used only two complex (four
real valued) input components.
Semi-supervised Ranking Pursuit
Authors: Evgeni Tsivtsivadze, Tom Heskes
Date: 2013-07-02 00:00:00
Tasks: regression

We propose a novel sparse preference learning/ranking algorithm. Our
algorithm approximates the true utility function by a weighted sum of basis
functions using the squared loss on pairs of data points, and is a
generalization of the kernel matching pursuit method. It can operate both in a
supervised and a semi-supervised setting and allows efficient search for
multiple, near-optimal solutions. Furthermore, we describe the extension of the
algorithm suitable for combined ranking and regression tasks. In our
experiments we demonstrate that the proposed algorithm outperforms several
state-of-the-art learning methods when taking into account unlabeled data and
performs comparably in a supervised learning scenario, while providing sparser
solutions.
Comparing various regression methods on ensemble strategies in differential evolution
Authors: Iztok Fister Jr., Iztok Fister, Janez Brest
Date: 2013-07-02 00:00:00
Tasks: regression

Differential evolution possesses a multitude of various strategies for
generating new trial solutions. Unfortunately, the best strategy is not known
in advance. Moreover, this strategy usually depends on the problem to be
solved. This paper suggests using various regression methods (like random
forest, extremely randomized trees, gradient boosting, decision trees, and a
generalized linear model) on ensemble strategies in differential evolution
algorithm by predicting the best differential evolution strategy during the
run. Comparing the preliminary results of this algorithm by optimizing a suite
of five well-known functions from literature, it was shown that using the
random forest regression method substantially outperformed the results of the
other regression methods.
Approximate Counting of Graphical Models Via MCMC Revisited
Authors: Jose M. Peña
Date: 2013-01-30 00:00:00

In Pe\~na (2007), MCMC sampling is applied to approximately calculate the
ratio of essential graphs (EGs) to directed acyclic graphs (DAGs) for up to 20
nodes. In the present paper, we extend that work from 20 to 31 nodes. We also
extend that work by computing the approximate ratio of connected EGs to
connected DAGs, of connected EGs to EGs, and of connected DAGs to DAGs.
Furthermore, we prove that the latter ratio is asymptotically 1. We also
discuss the implications of these results for learning DAGs from data.
Distributed Online Big Data Classification Using Context Information
Authors: Cem Tekin, Mihaela van der Schaar
Date: 2013-07-02 00:00:00
Tasks: Classification, General Classification

Distributed, online data mining systems have emerged as a result of
applications requiring analysis of large amounts of correlated and
high-dimensional data produced by multiple distributed data sources. We propose
a distributed online data classification framework where data is gathered by
distributed data sources and processed by a heterogeneous set of distributed
learners which learn online, at run-time, how to classify the different data
streams either by using their locally available classification functions or by
helping each other by classifying each other's data. Importantly, since the
data is gathered at different locations, sending the data to another learner to
process incurs additional costs such as delays, and hence this will be only
beneficial if the benefits obtained from a better classification will exceed
the costs. We model the problem of joint classification by the distributed and
heterogeneous learners from multiple data sources as a distributed contextual
bandit problem where each data is characterized by a specific context. We
develop a distributed online learning algorithm for which we can prove
sublinear regret. Compared to prior work in distributed online data mining, our
work is the first to provide analytic regret results characterizing the
performance of the proposed algorithm.
Regularized Spherical Polar Fourier Diffusion MRI with Optimal Dictionary Learning
Authors: Jian Cheng, Tianzi Jiang, Rachid Deriche, Dinggang Shen, Pew-Thian Yap
Date: 2013-07-02 00:00:00
Tasks: compressed sensing, Dictionary Learning, Diffusion  MRI

Compressed Sensing (CS) takes advantage of signal sparsity or compressibility
and allows superb signal reconstruction from relatively few measurements. Based
on CS theory, a suitable dictionary for sparse representation of the signal is
required. In diffusion MRI (dMRI), CS methods were proposed to reconstruct
diffusion-weighted signal and the Ensemble Average Propagator (EAP), and there
are two kinds of Dictionary Learning (DL) methods: 1) Discrete Representation
DL (DR-DL), and 2) Continuous Representation DL (CR-DL). DR-DL is susceptible
to numerical inaccuracy owing to interpolation and regridding errors in a
discretized q-space. In this paper, we propose a novel CR-DL approach, called
Dictionary Learning - Spherical Polar Fourier Imaging (DL-SPFI) for effective
compressed-sensing reconstruction of the q-space diffusion-weighted signal and
the EAP. In DL-SPFI, an dictionary that sparsifies the signal is learned from
the space of continuous Gaussian diffusion signals. The learned dictionary is
then adaptively applied to different voxels using a weighted LASSO framework
for robust signal reconstruction. The adaptive dictionary is proved to be
optimal. Compared with the start-of-the-art CR-DL and DR-DL methods proposed by
Merlet et al. and Bilgic et al., espectively, our work offers the following
advantages. First, the learned dictionary is proved to be optimal for Gaussian
diffusion signals. Second, to our knowledge, this is the first work to learn a
voxel-adaptive dictionary. The importance of the adaptive dictionary in EAP
reconstruction will be demonstrated theoretically and empirically. Third,
optimization in DL-SPFI is only performed in a small subspace resided by the
SPF coefficients, as opposed to the q-space approach utilized by Merlet et al.
The experiment results demonstrate the advantages of DL-SPFI over the original
SPF basis and Bilgic et al.'s method.
Submodularity of a Set Label Disagreement Function
Authors: Toufiq Parag
Date: 2013-07-02 00:00:00
Tasks: Combinatorial Optimization

A set label disagreement function is defined over the number of variables
that deviates from the dominant label. The dominant label is the value assumed
by the largest number of variables within a set of binary variables. The
submodularity of a certain family of set label disagreement function is
discussed in this manuscript. Such disagreement function could be utilized as a
cost function in combinatorial optimization approaches for problems defined
over hypergraphs.
Discovering the Markov network structure
Authors: Edith Kovács, Tamás Szántai
Date: 2013-07-02 00:00:00

In this paper a new proof is given for the supermodularity of information
content. Using the decomposability of the information content an algorithm is
given for discovering the Markov network graph structure endowed by the
pairwise Markov property of a given probability distribution. A discrete
probability distribution is given for which the equivalence of
Hammersley-Clifford theorem is fulfilled although some of the possible vector
realizations are taken on with zero probability. Our algorithm for discovering
the pairwise Markov network is illustrated on this example, too.
Improving Pointwise Mutual Information (PMI) by Incorporating Significant Co-occurrence
Authors: Om P. Damani
Date: 2013-07-02 00:00:00

We design a new co-occurrence based word association measure by incorporating
the concept of significant cooccurrence in the popular word association measure
Pointwise Mutual Information (PMI). By extensive experiments with a large
number of publicly available datasets we show that the newly introduced measure
performs better than other co-occurrence based measures and despite being
resource-light, compares well with the best known resource-heavy distributional
similarity and knowledge based word association measures. We investigate the
source of this performance improvement and find that of the two types of
significant co-occurrence - corpus-level and document-level, the concept of
corpus level significance combined with the use of document counts in place of
word counts is responsible for all the performance gains observed. The concept
of document level significance is not helpful for PMI adaptation.
The Orchive : Data mining a massive bioacoustic archive
Authors: Steven Ness, Helena Symonds, Paul Spong, George Tzanetakis
Date: 2013-07-02 00:00:00

The Orchive is a large collection of over 20,000 hours of audio recordings
from the OrcaLab research facility located off the northern tip of Vancouver
Island. It contains recorded orca vocalizations from the 1980 to the present
time and is one of the largest resources of bioacoustic data in the world. We
have developed a web-based interface that allows researchers to listen to these
recordings, view waveform and spectral representations of the audio, label
clips with annotations, and view the results of machine learning classifiers
based on automatic audio features extraction. In this paper we describe such
classifiers that discriminate between background noise, orca calls, and the
voice notes that are present in most of the tapes. Furthermore we show
classification results for individual calls based on a previously existing orca
call catalog. We have also experimentally investigated the scalability of
classifiers over the entire Orchive.
A non-parametric conditional factor regression model for high-dimensional input and response
Authors: Ava Bargi, Richard Yi Da Xu, Massimo Piccardi
Date: 2013-07-02 00:00:00
Tasks: Dimensionality Reduction, regression

In this paper, we propose a non-parametric conditional factor regression
(NCFR)model for domains with high-dimensional input and response. NCFR enhances
linear regression in two ways: a) introducing low-dimensional latent factors
leading to dimensionality reduction and b) integrating an Indian Buffet Process
as a prior for the latent factors to derive unlimited sparse dimensions.
Experimental results comparing NCRF to several alternatives give evidence to
remarkable prediction performance.
Syntactic sensitive complexity for symbol-free sequence
Authors: Cheng-Yuan Liou, Bo-Shiang Huang, Daw-Ran Liou, Alex A. Simak
Date: 2013-07-01 00:00:00
Tasks: Anomaly Detection

This work uses the L-system to construct a tree structure for the text
sequence and derives its complexity. It serves as a measure of structural
complexity of the text. It is applied to anomaly detection in data
transmission.
Exchanging OWL 2 QL Knowledge Bases
Authors: Marcelo Arenas, Elena Botoeva, Diego Calvanese, Vladislav Ryzhikov
Date: 2013-04-21 00:00:00

Knowledge base exchange is an important problem in the area of data exchange
and knowledge representation, where one is interested in exchanging information
between a source and a target knowledge base connected through a mapping. In
this paper, we study this fundamental problem for knowledge bases and mappings
expressed in OWL 2 QL, the profile of OWL 2 based on the description logic
DL-Lite_R. More specifically, we consider the problem of computing universal
solutions, identified as one of the most desirable translations to be
materialized, and the problem of computing UCQ-representations, which optimally
capture in a target TBox the information that can be extracted from a source
TBox and a mapping by means of unions of conjunctive queries. For the former we
provide a novel automata-theoretic technique, and complexity results that range
from NP to EXPTIME, while for the latter we show NLOGSPACE-completeness.
Evaluation Measures for Hierarchical Classification: a unified view and novel approaches
Authors: Aris Kosmopoulos, Ioannis Partalas, Eric Gaussier, Georgios Paliouras, Ion Androutsopoulos
Date: 2013-06-28 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

Hierarchical classification addresses the problem of classifying items into a
hierarchy of classes. An important issue in hierarchical classification is the
evaluation of different classification algorithms, which is complicated by the
hierarchical relations among the classes. Several evaluation measures have been
proposed for hierarchical classification using the hierarchy in different ways.
This paper studies the problem of evaluation in hierarchical classification by
analyzing and abstracting the key components of the existing performance
measures. It also proposes two alternative generic views of hierarchical
evaluation and introduces two corresponding novel measures. The proposed
measures, along with the state-of-the art ones, are empirically tested on three
large datasets from the domain of text classification. The empirical results
illustrate the undesirable behavior of existing approaches and how the proposed
methods overcome most of these methods across a range of cases.
Challenges in Representation Learning: A report on three machine learning contests
Authors: Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron Courville, Mehdi Mirza, Ben Hamner, Will Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, Yingbo Zhou, Chetan Ramaiah, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Zhang Chuang, Yoshua Bengio
Date: 2013-07-01 00:00:00
Tasks: BIG-bench Machine Learning, Facial Expression Recognition, Facial Expression Recognition (FER), Representation Learning

The ICML 2013 Workshop on Challenges in Representation Learning focused on
three challenges: the black box learning challenge, the facial expression
recognition challenge, and the multimodal learning challenge. We describe the
datasets created for these challenges and summarize the results of the
competitions. We provide suggestions for organizers of future challenges and
some comments on what kind of knowledge can be gained from machine learning
competitions.
Gaussian Process Conditional Copulas with Applications to Financial Time Series
Authors: José Miguel Hernández-Lobato, James Robert Lloyd, Daniel Hernández-Lobato
Date: 2013-07-01 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Time Series, Time Series Analysis

The estimation of dependencies between multiple variables is a central
problem in the analysis of financial time series. A common approach is to
express these dependencies in terms of a copula function. Typically the copula
function is assumed to be constant but this may be inaccurate when there are
covariates that could have a large influence on the dependence structure of the
data. To account for this, a Bayesian framework for the estimation of
conditional copulas is proposed. In this framework the parameters of a copula
are non-linearly related to some arbitrary conditioning variables. We evaluate
the ability of our method to predict time-varying dependencies on several
equities and currencies and observe consistent performance gains compared to
static copula models and other time-varying copula methods.
Quantifying the Impact of Parameter Tuning on Nature-Inspired Algorithms
Authors: Matthew Crossley, Andy Nisbet, Martyn Amos
Date: 2013-05-03 00:00:00

The problem of parameterization is often central to the effective deployment
of nature-inspired algorithms. However, finding the optimal set of parameter
values for a combination of problem instance and solution method is highly
challenging, and few concrete guidelines exist on how and when such tuning may
be performed. Previous work tends to either focus on a specific algorithm or
use benchmark problems, and both of these restrictions limit the applicability
of any findings. Here, we examine a number of different algorithms, and study
them in a "problem agnostic" fashion (i.e., one that is not tied to specific
instances) by considering their performance on fitness landscapes with varying
characteristics. Using this approach, we make a number of observations on which
algorithms may (or may not) benefit from tuning, and in which specific
circumstances.
Solving Weighted Voting Game Design Problems Optimally: Representations, Synthesis, and Enumeration
Authors: Bart de Keijzer, Tomas B. Klos, Yingqian Zhang
Date: 2012-04-23 00:00:00
Tasks: Game Design

We study the inverse power index problem for weighted voting games: the
problem of finding a weighted voting game in which the power of the players is
as close as possible to a certain target distribution. Our goal is to find
algorithms that solve this problem exactly. Thereto, we study various
subclasses of simple games, and their associated representation methods. We
survey algorithms and impossibility results for the synthesis problem, i.e.,
converting a representation of a simple game into another representation.
  We contribute to the synthesis problem by showing that it is impossible to
compute in polynomial time the list of ceiling coalitions (also known as
shift-maximal losing coalitions) of a game from its list of roof coalitions
(also known as shift-minimal winning coalitions), and vice versa.
  Then, we proceed by studying the problem of enumerating the set of weighted
voting games. We present first a naive algorithm for this, running in doubly
exponential time. Using our knowledge of the synthesis problem, we then improve
on this naive algorithm, and we obtain an enumeration algorithm that runs in
quadratic exponential time (that is, O(2^(n^2) p(n)) for a polynomial p).
Moreover, we show that this algorithm runs in output-polynomial time, making it
the best possible enumeration algorithm up to a polynomial factor.
  Finally, we propose an exact anytime algorithm for the inverse power index
problem that runs in exponential time. This algorithm is straightforward and
general: it computes the error for each game enumerated, and outputs the game
that minimizes this error. By the genericity of our approach, our algorithm can
be used to find a weighted voting game that optimizes any exponential time
computable function. We implement our algorithm for the case of the normalized
Banzhaf index, and we perform experiments in order to study performance and
error convergence.