pwc-archive/papers-with-abstracts huggingface dataset:
Ranking Algorithms by Performance
Authors: Lars Kotthoff
Date: 2013-11-18 00:00:00

A common way of doing algorithm selection is to train a machine learning
model and predict the best algorithm from a portfolio to solve a particular
problem. While this method has been highly successful, choosing only a single
algorithm has inherent limitations -- if the choice was bad, no remedial action
can be taken and parallelism cannot be exploited, to name but a few problems.
In this paper, we investigate how to predict the ranking of the portfolio
algorithms on a particular problem. This information can be used to choose the
single best algorithm, but also to allocate resources to the algorithms
according to their rank. We evaluate a range of approaches to predict the
ranking of a set of algorithms on a problem. We furthermore introduce a
framework for categorizing ranking predictions that allows to judge the
expressiveness of the predictive output. Our experimental evaluation
demonstrates on a range of data sets from the literature that it is beneficial
to consider the relationship between algorithms when predicting rankings. We
furthermore show that relatively naive approaches deliver rankings of good
quality already.
Guided Random Forest in the RRF Package
Authors: Houtao Deng
Date: 2013-06-02 00:00:00
Tasks: feature selection

Random Forest (RF) is a powerful supervised learner and has been popularly
used in many applications such as bioinformatics.
  In this work we propose the guided random forest (GRF) for feature selection.
Similar to a feature selection method called guided regularized random forest
(GRRF), GRF is built using the importance scores from an ordinary RF. However,
the trees in GRRF are built sequentially, are highly correlated and do not
allow for parallel computing, while the trees in GRF are built independently
and can be implemented in parallel. Experiments on 10 high-dimensional gene
data sets show that, with a fixed parameter value (without tuning the
parameter), RF applied to features selected by GRF outperforms RF applied to
all features on 9 data sets and 7 of them have significant differences at the
0.05 level. Therefore, both accuracy and interpretability are significantly
improved. GRF selects more features than GRRF, however, leads to better
classification accuracy. Note in this work the guided random forest is guided
by the importance scores from an ordinary random forest, however, it can also
be guided by other methods such as human insights (by specifying $\lambda_i$).
GRF can be used in "RRF" v1.4 (and later versions), a package that also
includes the regularized random forest methods.
Reflection methods for user-friendly submodular optimization
Authors: Stefanie Jegelka, Francis Bach, Suvrit Sra
Date: 2013-11-18 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Image Segmentation, Semantic Segmentation

Recently, it has become evident that submodularity naturally captures widely
occurring concepts in machine learning, signal processing and computer vision.
Consequently, there is need for efficient optimization procedures for
submodular functions, especially for minimization problems. While general
submodular minimization is challenging, we propose a new method that exploits
existing decomposability of submodular functions. In contrast to previous
approaches, our method is neither approximate, nor impractical, nor does it
need any cumbersome parameter tuning. Moreover, it is easy to implement and
parallelize. A key component of our method is a formulation of the discrete
submodular minimization problem as a continuous best approximation problem that
is solved through a sequence of reflections, and its solution can be easily
thresholded to obtain an optimal discrete solution. This method solves both the
continuous and discrete formulations of the problem, and therefore has
applications in learning, inference, and reconstruction. In our experiments, we
illustrate the benefits of our method on two image segmentation tasks.
Contour polygonal approximation using shortest path in networks
Authors: André Ricardo Backes, Dalcimar Casanova, Odemir Martinez Bruno
Date: 2013-11-18 00:00:00

Contour polygonal approximation is a simplified representation of a contour
by line segments, so that the main characteristics of the contour remain in a
small number of line segments. This paper presents a novel method for polygonal
approximation based on the Complex Networks theory. We convert each point of
the contour into a vertex, so that we model a regular network. Then we
transform this network into a Small-World Complex Network by applying some
transformations over its edges. By analyzing of network properties, especially
the geodesic path, we compute the polygonal approximation. The paper presents
the main characteristics of the method, as well as its functionality. We
evaluate the proposed method using benchmark contours, and compare its results
with other polygonal approximation methods.
On the definition of a general learning system with user-defined operators
Authors: Fernando Martínez-Plumed, Cèsar Ferri, José Hernández-Orallo, María-José Ramírez-Quintana
Date: 2013-11-18 00:00:00
Tasks: BIG-bench Machine Learning, Reinforcement Learning, Structured Prediction, Transfer Learning

In this paper, we push forward the idea of machine learning systems whose
operators can be modified and fine-tuned for each problem. This allows us to
propose a learning paradigm where users can write (or adapt) their operators,
according to the problem, data representation and the way the information
should be navigated. To achieve this goal, data instances, background
knowledge, rules, programs and operators are all written in the same functional
language, Erlang. Since changing operators affect how the search space needs to
be explored, heuristics are learnt as a result of a decision process based on
reinforcement learning where each action is defined as a choice of operator and
rule. As a result, the architecture can be seen as a 'system for writing
machine learning systems' or to explore new operators where the policy reuse
(as a kind of transfer learning) is allowed. States and actions are represented
in a Q matrix which is actually a table, from which a supervised model is
learnt. This makes it possible to have a more flexible mapping between old and
new problems, since we work with an abstraction of rules and actions. We
include some examples sharing reuse and the application of the system gErl to
IQ problems. In order to evaluate gErl, we will test it against some structured
problems: a selection of IQ test tasks and some experiments on some structured
prediction problems (list patterns).
A Visibility Graph Averaging Aggregation Operator
Authors: Shiyu Chen, Yong Hu, Sankaran Mahadevan, Yong Deng
Date: 2013-11-17 00:00:00
Tasks: Time Series, Time Series Analysis

The problem of aggregation is considerable importance in many disciplines. In
this paper, a new type of operator called visibility graph averaging (VGA)
aggregation operator is proposed. This proposed operator is based on the
visibility graph which can convert a time series into a graph. The weights are
obtained according to the importance of the data in the visibility graph.
Finally, the VGA operator is used in the analysis of the TAIEX database to
illustrate that it is practical and compared with the classic aggregation
operators, it shows its advantage that it not only implements the aggregation
of the data purely, but also conserves the time information, and meanwhile, the
determination of the weights is more reasonable.
Replica Exchange using q-Gaussian Swarm Quantum Particle Intelligence Method
Authors: Hiqmet Kamberaj
Date: 2013-11-17 00:00:00
Tasks: Diversity

We present a newly developed Replica Exchange algorithm using q -Gaussian
Swarm Quantum Particle Optimization (REX@q-GSQPO) method for solving the
problem of finding the global optimum. The basis of the algorithm is to run
multiple copies of independent swarms at different values of q parameter. Based
on an energy criterion, chosen to satisfy the detailed balance, we are swapping
the particle coordinates of neighboring swarms at regular iteration intervals.
The swarm replicas with high q values are characterized by high diversity of
particles allowing escaping local minima faster, while the low q replicas,
characterized by low diversity of particles, are used to sample more
efficiently the local basins. We compare the new algorithm with the standard
Gaussian Swarm Quantum Particle Optimization (GSQPO) and q-Gaussian Swarm
Quantum Particle Optimization (q-GSQPO) algorithms, and we found that the new
algorithm is more robust in terms of the number of fitness function calls, and
more efficient in terms ability convergence to the global minimum. In
additional, we also provide a method of optimally allocating the swarm replicas
among different q values. Our algorithm is tested for three benchmark
functions, which are known to be multimodal problems, at different
dimensionalities. In addition, we considered a polyalanine peptide of 12
residues modeled using a G\=o coarse-graining potential energy function.
Sigma Point Belief Propagation
Authors: Florian Meyer, Ondrej Hlinka, Franz Hlawatsch
Date: 2013-09-02 00:00:00
Tasks: Bayesian Inference

The sigma point (SP) filter, also known as unscented Kalman filter, is an
attractive alternative to the extended Kalman filter and the particle filter.
Here, we extend the SP filter to nonsequential Bayesian inference corresponding
to loopy factor graphs. We propose sigma point belief propagation (SPBP) as a
low-complexity approximation of the belief propagation (BP) message passing
scheme. SPBP achieves approximate marginalizations of posterior distributions
corresponding to (generally) loopy factor graphs. It is well suited for
decentralized inference because of its low communication requirements. For a
decentralized, dynamic sensor localization problem, we demonstrate that SPBP
can outperform nonparametric (particle-based) BP while requiring significantly
less computations and communications.
Towards Big Topic Modeling
Authors: Jian-Feng Yan, Jia Zeng, Zhi-Qiang Liu, Yang Gao
Date: 2013-11-17 00:00:00

To solve the big topic modeling problem, we need to reduce both time and
space complexities of batch latent Dirichlet allocation (LDA) algorithms.
Although parallel LDA algorithms on the multi-processor architecture have low
time and space complexities, their communication costs among processors often
scale linearly with the vocabulary size and the number of topics, leading to a
serious scalability problem. To reduce the communication complexity among
processors for a better scalability, we propose a novel communication-efficient
parallel topic modeling architecture based on power law, which consumes orders
of magnitude less communication time when the number of topics is large. We
combine the proposed communication-efficient parallel architecture with the
online belief propagation (OBP) algorithm referred to as POBP for big topic
modeling tasks. Extensive empirical results confirm that POBP has the following
advantages to solve the big topic modeling problem: 1) high accuracy, 2)
communication-efficient, 3) fast speed, and 4) constant memory usage when
compared with recent state-of-the-art parallel LDA algorithms on the
multi-processor architecture.
Multilabel Classification through Random Graph Ensembles
Authors: Hongyu Su, Juho Rousu
Date: 2013-10-31 00:00:00
Tasks: Classification, Diversity, Ensemble Learning, General Classification

We present new methods for multilabel classification, relying on ensemble
learning on a collection of random output graphs imposed on the multilabel and
a kernel-based structured output learner as the base classifier. For ensemble
learning, differences among the output graphs provide the required base
classifier diversity and lead to improved performance in the increasing size of
the ensemble. We study different methods of forming the ensemble prediction,
including majority voting and two methods that perform inferences over the
graph structures before or after combining the base models into the ensemble.
We compare the methods against the state-of-the-art machine learning approaches
on a set of heterogeneous multilabel benchmark problems, including multilabel
AdaBoost, convex multitask feature learning, as well as single target learning
approaches represented by Bagging and SVM. In our experiments, the random graph
ensembles are very competitive and robust, ranking first or second on most of
the datasets. Overall, our results show that random graph ensembles are viable
alternatives to flat multilabel and multitask learners.
The Optimization of Running Queries in Relational Databases Using ANT-Colony Algorithm
Authors: Adel Alinezhad Kolaei, Marzieh Ahmadzadeh
Date: 2013-11-16 00:00:00
Tasks: Management

The issue of optimizing queries is a cost-sensitive process and with respect
to the number of associated tables in a query, its number of permutations grows
exponentially. On one hand, in comparison with other operators in relational
database, join operator is the most difficult and complicated one in terms of
optimization for reducing its runtime. Accordingly, various algorithms have so
far been proposed to solve this problem. On the other hand, the success of any
database management system (DBMS) means exploiting the query model. In the
current paper, the heuristic ant algorithm has been proposed to solve this
problem and improve the runtime of join operation. Experiments and observed
results reveal the efficiency of this algorithm compared to its similar
algorithms.
A hybrid decision support system : application on healthcare
Authors: Abdelhak Mansoul, Baghdad Atmani, Sofia Benbelkacem
Date: 2013-11-16 00:00:00

Many systems based on knowledge, especially expert systems for medical
decision support have been developed. Only systems are based on production
rules, and cannot learn and evolve only by updating them. In addition, taking
into account several criteria induces an exorbitant number of rules to be
injected into the system. It becomes difficult to translate medical knowledge
or a support decision as a simple rule. Moreover, reasoning based on generic
cases became classic and can even reduce the range of possible solutions. To
remedy that, we propose an approach based on using a multi-criteria decision
guided by a case-based reasoning (CBR) approach.
A new bio-inspired method for remote sensing imagery classification
Authors: Amghar Yasmina Teldja, Fizazi Hadria
Date: 2013-02-11 00:00:00
Tasks: Clustering, General Classification

The problem of supervised classification of the satellite image is considered
to be the task of grouping pixels into a number of homogeneous regions in space
intensity. This paper proposes a novel approach that combines a radial basic
function clustering network with a growing neural gas include utility factor
classifier to yield improved solutions, obtained with previous networks. The
double objective technique is first used to the development of a method to
perform the satellite images classification, and finally, the implementation to
address the issue of the number of nodes in the hidden layer of the classic
Radial Basis functions network. Results demonstrating the effectiveness of the
proposed technique are provided for numeric remote sensing imagery. Moreover,
the remotely sensed image of Oran city in Algeria has been classified using the
proposed technique to establish its utility.
Methods for Integrating Knowledge with the Three-Weight Optimization Algorithm for Hybrid Cognitive Processing
Authors: Nate Derbinsky, José Bento, Jonathan S. Yedidia
Date: 2013-11-16 00:00:00

In this paper we consider optimization as an approach for quickly and
flexibly developing hybrid cognitive capabilities that are efficient, scalable,
and can exploit knowledge to improve solution speed and quality. In this
context, we focus on the Three-Weight Algorithm, which aims to solve general
optimization problems. We propose novel methods by which to integrate knowledge
with this algorithm to improve expressiveness, efficiency, and scaling, and
demonstrate these techniques on two example problems (Sudoku and circle
packing).
A generalized evidence distance
Authors: Hongming Mo, Xiaoyan Su, Yong Hu, Yong Deng
Date: 2013-11-16 00:00:00

Dempster-Shafer theory of evidence (D-S theory) is widely used in uncertain
information process. The basic probability assignment(BPA) is a key element in
D-S theory. How to measure the distance between two BPAs is an open issue. In
this paper, a new method to measure the distance of two BPAs is proposed. The
proposed method is a generalized of existing evidence distance. Numerical
examples are illustrated that the proposed method can overcome the shortcomings
of existing methods.
A Comparative Study of Histogram Equalization Based Image Enhancement Techniques for Brightness Preservation and Contrast Enhancement
Authors: Omprakash Patel, Yogendra P. S. Maravi, Sanjeev Sharma
Date: 2013-11-16 00:00:00
Tasks: Image Enhancement

Histogram Equalization is a contrast enhancement technique in the image
processing which uses the histogram of image. However histogram equalization is
not the best method for contrast enhancement because the mean brightness of the
output image is significantly different from the input image. There are several
extensions of histogram equalization has been proposed to overcome the
brightness preservation challenge. Contrast enhancement using brightness
preserving bi-histogram equalization (BBHE) and Dualistic sub image histogram
equalization (DSIHE) which divides the image histogram into two parts based on
the input mean and median respectively then equalizes each sub histogram
independently. This paper provides review of different popular histogram
equalization techniques and experimental study based on the absolute mean
brightness error (AMBE), peak signal to noise ratio (PSNR), Structure
similarity index (SSI) and Entropy.
Nonparametric Link Prediction in Large Scale Dynamic Networks
Authors: Purnamrita Sarkar, Deepayan Chakrabarti, Michael Jordan
Date: 2011-09-06 00:00:00
Tasks: Link Prediction, Prediction

We propose a nonparametric approach to link prediction in large-scale dynamic
networks. Our model uses graph-based features of pairs of nodes as well as
those of their local neighborhoods to predict whether those nodes will be
linked at each time step. The model allows for different types of evolution in
different parts of the graph (e.g, growing or shrinking communities). We focus
on large-scale graphs and present an implementation of our model that makes use
of locality-sensitive hashing to allow it to be scaled to large problems.
Experiments with simulated data as well as five real-world dynamic graphs show
that we outperform the state of the art, especially when sharp fluctuations or
nonlinearities are present. We also establish theoretical properties of our
estimator, in particular consistency and weak convergence, the latter making
use of an elaboration of Stein's method for dependency graphs.
Inferring the Origin Locations of Tweets with Quantitative Confidence
Authors: Reid Priedhorsky, Aron Culotta, Sara Y. Del Valle
Date: 2013-05-16 00:00:00
Tasks: Management

Social Internet content plays an increasingly critical role in many domains,
including public health, disaster management, and politics. However, its
utility is limited by missing geographic information; for example, fewer than
1.6% of Twitter messages (tweets) contain a geotag. We propose a scalable,
content-based approach to estimate the location of tweets using a novel yet
simple variant of gaussian mixture models. Further, because real-world
applications depend on quantified uncertainty for such estimates, we propose
novel metrics of accuracy, precision, and calibration, and we evaluate our
approach accordingly. Experiments on 13 million global, comprehensively
multi-lingual tweets show that our approach yields reliable, well-calibrated
results competitive with previous computationally intensive methods. We also
show that a relatively small number of training data are required for good
estimates (roughly 30,000 tweets) and models are quite time-invariant
(effective on tweets many weeks newer than the training set). Finally, we show
that toponyms and languages with small geographic footprint provide the most
useful location signals.
Inferring Multilateral Relations from Dynamic Pairwise Interactions
Authors: Aaron Schein, Juston Moore, Hanna Wallach
Date: 2013-11-15 00:00:00

Correlations between anomalous activity patterns can yield pertinent
information about complex social processes: a significant deviation from normal
behavior, exhibited simultaneously by multiple pairs of actors, provides
evidence for some underlying relationship involving those pairs---i.e., a
multilateral relation. We introduce a new nonparametric Bayesian latent
variable model that explicitly captures correlations between anomalous
interaction counts and uses these shared deviations from normal activity
patterns to identify and characterize multilateral relations. We showcase our
model's capabilities using the newly curated Global Database of Events,
Location, and Tone, a dataset that has seen considerable interest in the social
sciences and the popular press, but which has is largely unexplored by the
machine learning community. We provide a detailed analysis of the latent
structure inferred by our model and show that the multilateral relations
correspond to major international events and long-term international
relationships. These findings lead us to recommend our model for any
data-driven analysis of interaction networks where dynamic interactions over
the edges provide evidence for latent social structure.
The STONE Transform: Multi-Resolution Image Enhancement and Real-Time Compressive Video
Authors: Tom Goldstein, Lina Xu, Kevin F. Kelly, Richard Baraniuk
Date: 2013-11-14 00:00:00
Tasks: compressed sensing, Compressive Sensing, Image Enhancement

Compressed sensing enables the reconstruction of high-resolution signals from
under-sampled data. While compressive methods simplify data acquisition, they
require the solution of difficult recovery problems to make use of the
resulting measurements. This article presents a new sensing framework that
combines the advantages of both conventional and compressive sensing. Using the
proposed \stone transform, measurements can be reconstructed instantly at
Nyquist rates at any power-of-two resolution. The same data can then be
"enhanced" to higher resolutions using compressive methods that leverage
sparsity to "beat" the Nyquist limit. The availability of a fast direct
reconstruction enables compressive measurements to be processed on small
embedded devices. We demonstrate this by constructing a real-time compressive
video camera.
HEVAL: Yet Another Human Evaluation Metric
Authors: Nisheeth Joshi, Iti Mathur, Hemant Darbari, Ajai Kumar
Date: 2013-11-15 00:00:00
Tasks: Machine Translation, Translation

Machine translation evaluation is a very important activity in machine
translation development. Automatic evaluation metrics proposed in literature
are inadequate as they require one or more human reference translations to
compare them with output produced by machine translation. This does not always
give accurate results as a text can have several different translations. Human
evaluation metrics, on the other hand, lacks inter-annotator agreement and
repeatability. In this paper we have proposed a new human evaluation metric
which addresses these issues. Moreover this metric also provides solid grounds
for making sound assumptions on the quality of the text produced by a machine
translation.