pwc-archive/papers-with-abstracts huggingface dataset:
A Hybrid Feature Selection Method to Improve Performance of a Group of Classification Algorithms
Authors: Mehdi Naseriparsa, Amir-masoud Bidgoli, Touraj Varaee
Date: 2014-03-08 00:00:00
Tasks: feature selection, General Classification

In this paper a hybrid feature selection method is proposed which takes
advantages of wrapper subset evaluation with a lower cost and improves the
performance of a group of classifiers. The method uses combination of sample
domain filtering and resampling to refine the sample domain and two feature
subset evaluation methods to select reliable features. This method utilizes
both feature space and sample domain in two phases. The first phase filters and
resamples the sample domain and the second phase adopts a hybrid procedure by
information gain, wrapper subset evaluation and genetic search to find the
optimal feature space. Experiments carried out on different types of datasets
from UCI Repository of Machine Learning databases and the results show a rise
in the average performance of five classifiers (Naive Bayes, Logistic,
Multilayer Perceptron, Best First Decision Tree and JRIP) simultaneously and
the classification error for these classifiers decreases considerably. The
experiments also show that this method outperforms other feature selection
methods with a lower cost.
Improving Performance of a Group of Classification Algorithms Using Resampling and Feature Selection
Authors: Mehdi Naseriparsa, Amir-masoud Bidgoli, Touraj Varaee
Date: 2014-03-08 00:00:00
Tasks: Classification, feature selection, General Classification

In recent years the importance of finding a meaningful pattern from huge
datasets has become more challenging. Data miners try to adopt innovative
methods to face this problem by applying feature selection methods. In this
paper we propose a new hybrid method in which we use a combination of
resampling, filtering the sample domain and wrapper subset evaluation method
with genetic search to reduce dimensions of Lung-Cancer dataset that we
received from UCI Repository of Machine Learning databases. Finally, we apply
some well- known classification algorithms (Na\"ive Bayes, Logistic, Multilayer
Perceptron, Best First Decision Tree and JRIP) to the resulting dataset and
compare the results and prediction rates before and after the application of
our feature selection method on that dataset. The results show a substantial
progress in the average performance of five classification algorithms
simultaneously and the classification error for these classifiers decreases
considerably. The experiments also show that this method outperforms other
feature selection methods with a lower cost.
Multi-label ensemble based on variable pairwise constraint projection
Authors: Ping Li, Hong Li, Min Wu
Date: 2014-03-08 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Multi-label classification has attracted an increasing amount of attention in
recent years. To this end, many algorithms have been developed to classify
multi-label data in an effective manner. However, they usually do not consider
the pairwise relations indicated by sample labels, which actually play
important roles in multi-label classification. Inspired by this, we naturally
extend the traditional pairwise constraints to the multi-label scenario via a
flexible thresholding scheme. Moreover, to improve the generalization ability
of the classifier, we adopt a boosting-like strategy to construct a multi-label
ensemble from a group of base classifiers. To achieve these goals, this paper
presents a novel multi-label classification framework named Variable Pairwise
Constraint projection for Multi-label Ensemble (VPCME). Specifically, we take
advantage of the variable pairwise constraint projection to learn a
lower-dimensional data representation, which preserves the correlations between
samples and labels. Thereafter, the base classifiers are trained in the new
data space. For the boosting-like strategy, we employ both the variable
pairwise constraints and the bootstrap steps to diversify the base classifiers.
Empirical studies have shown the superiority of the proposed method in
comparison with other approaches.
Quality-based Multimodal Classification Using Tree-Structured Sparsity
Authors: Soheil Bahrampour, Asok Ray, Nasser M. Nasrabadi, Kenneth W. Jenkins
Date: 2014-03-08 00:00:00
Proceeding: CVPR 2014 6
Tasks: Classification, Face Recognition, General Classification

Recent studies have demonstrated advantages of information fusion based on
sparsity models for multimodal classification. Among several sparsity models,
tree-structured sparsity provides a flexible framework for extraction of
cross-correlated information from different sources and for enforcing group
sparsity at multiple granularities. However, the existing algorithm only solves
an approximated version of the cost functional and the resulting solution is
not necessarily sparse at group levels. This paper reformulates the
tree-structured sparse model for multimodal classification task. An accelerated
proximal algorithm is proposed to solve the optimization problem, which is an
efficient tool for feature-level fusion among either homogeneous or
heterogeneous sources of information. In addition, a (fuzzy-set-theoretic)
possibilistic scheme is proposed to weight the available modalities, based on
their respective reliability, in a joint optimization problem for finding the
sparsity codes. This approach provides a general framework for quality-based
fusion that offers added robustness to several sparsity-based multimodal
classification algorithms. To demonstrate their efficacy, the proposed methods
are evaluated on three different applications - multiview face recognition,
multimodal face recognition, and target classification.
Zero-Shot Learning for Semantic Utterance Classification
Authors: Yann N. Dauphin, Gokhan Tur, Dilek Hakkani-Tur, Larry Heck
Date: 2013-12-20 00:00:00
Tasks: Classification, General Classification, Zero-Shot Learning

We propose a novel zero-shot learning method for semantic utterance
classification (SUC). It learns a classifier $f: X \to Y$ for problems where
none of the semantic categories $Y$ are present in the training set. The
framework uncovers the link between categories and utterances using a semantic
space. We show that this semantic space can be learned by deep neural networks
trained on large amounts of search engine query log data. More precisely, we
propose a novel method that can learn discriminative semantic features without
supervision. It uses the zero-shot learning framework to guide the learning of
the semantic features. We demonstrate the effectiveness of the zero-shot
semantic learning algorithm on the SUC dataset collected by (Tur, 2012).
Furthermore, we achieve state-of-the-art results by combining the semantic
features with a supervised method.
Becoming More Robust to Label Noise with Classifier Diversity
Authors: Michael R. Smith, Tony Martinez
Date: 2014-03-07 00:00:00
Tasks: Diversity

It is widely known in the machine learning community that class noise can be
(and often is) detrimental to inducing a model of the data. Many current
approaches use a single, often biased, measurement to determine if an instance
is noisy. A biased measure may work well on certain data sets, but it can also
be less effective on a broader set of data sets. In this paper, we present
noise identification using classifier diversity (NICD) -- a method for deriving
a less biased noise measurement and integrating it into the learning process.
To lessen the bias of the noise measure, NICD selects a diverse set of
classifiers (based on their predictions of novel instances) to determine which
instances are noisy. We examine NICD as a technique for filtering, instance
weighting, and selecting the base classifiers of a voting ensemble. We compare
NICD with several other noise handling techniques that do not consider
classifier diversity on a set of 54 data sets and 5 learning algorithms. NICD
significantly increases the classification accuracy over the other considered
approaches and is effective across a broad set of data sets and learning
algorithms.
Statistical Structure Learning, Towards a Robust Smart Grid
Authors: Hanie Sedghi, Edmond Jonckheere
Date: 2014-03-07 00:00:00

Robust control and maintenance of the grid relies on accurate data. Both PMUs
and state estimators are prone to false data injection attacks. Thus, it is
crucial to have a mechanism for fast and accurate detection of an agent
maliciously tampering with the data---for both preventing attacks that may lead
to blackouts, and for routine monitoring and control tasks of current and
future grids. We propose a decentralized false data injection detection scheme
based on Markov graph of the bus phase angles. We utilize the Conditional
Covariance Test (CCT) to learn the structure of the grid. Using the DC power
flow model, we show that under normal circumstances, and because of
walk-summability of the grid graph, the Markov graph of the voltage angles can
be determined by the power grid graph. Therefore, a discrepancy between
calculated Markov graph and learned structure should trigger the alarm. Local
grid topology is available online from the protection system and we exploit it
to check for mismatch. Should a mismatch be detected, we use correlation
anomaly score to detect the set of attacked nodes. Our method can detect the
most recent stealthy deception attack on the power grid that assumes knowledge
of bus-branch model of the system and is capable of deceiving the state
estimator, damaging power network observatory, control, monitoring, demand
response and pricing schemes. Specifically, under the stealthy deception
attack, the Markov graph of phase angles changes. In addition to detect a state
of attack, our method can detect the set of attacked nodes. To the best of our
knowledge, our remedy is the first to comprehensively detect this sophisticated
attack and it does not need additional hardware. Moreover, our detection scheme
is successful no matter the size of the attacked subset. Simulation of various
power networks confirms our claims.
Near-Optimally Teaching the Crowd to Classify
Authors: Adish Singla, Ilija Bogunovic, Gábor Bartók, Amin Karbasi, Andreas Krause
Date: 2014-02-10 00:00:00

How should we present training examples to learners to teach them
classification rules? This is a natural problem when training workers for
crowdsourcing labeling tasks, and is also motivated by challenges in
data-driven online education. We propose a natural stochastic model of the
learners, modeling them as randomly switching among hypotheses based on
observed feedback. We then develop STRICT, an efficient algorithm for selecting
examples to teach to workers. Our solution greedily maximizes a submodular
surrogate objective function in order to select examples to show to the
learners. We prove that our strategy is competitive with the optimal teaching
policy. Moreover, for the special case of linear separators, we prove that an
exponential reduction in error probability can be achieved. Our experiments on
simulated workers as well as three real image annotation tasks on Amazon
Mechanical Turk show the effectiveness of our teaching algorithm.
Analysis of Agglomerative Clustering
Authors: Marcel R. Ackermann, Johannes Blömer, Daniel Kuntze, Christian Sohler
Date: 2010-12-16 00:00:00
Tasks: Clustering

The diameter $k$-clustering problem is the problem of partitioning a finite
subset of $\mathbb{R}^d$ into $k$ subsets called clusters such that the maximum
diameter of the clusters is minimized. One early clustering algorithm that
computes a hierarchy of approximate solutions to this problem (for all values
of $k$) is the agglomerative clustering algorithm with the complete linkage
strategy. For decades, this algorithm has been widely used by practitioners.
However, it is not well studied theoretically. In this paper, we analyze the
agglomerative complete linkage clustering algorithm. Assuming that the
dimension $d$ is a constant, we show that for any $k$ the solution computed by
this algorithm is an $O(\log k)$-approximation to the diameter $k$-clustering
problem. Our analysis does not only hold for the Euclidean distance but for any
metric that is based on a norm. Furthermore, we analyze the closely related
$k$-center and discrete $k$-center problem. For the corresponding agglomerative
algorithms, we deduce an approximation factor of $O(\log k)$ as well.
The SAT-UNSAT transition in the adversarial SAT problem
Authors: Marco Bardoscia, Daniel Nagaj, Antonello Scardicchio
Date: 2013-10-03 00:00:00

Adversarial SAT (AdSAT) is a generalization of the satisfiability (SAT)
problem in which two players try to make a boolean formula true (resp. false)
by controlling their respective sets of variables. AdSAT belongs to a higher
complexity class in the polynomial hierarchy than SAT and therefore the nature
of the critical region and the transition are not easily paralleled to those of
SAT and worth of independent study. AdSAT also provides an upper bound for the
transition threshold of the quantum satisfiability problem (QSAT). We present a
complete algorithm for AdSAT, show that 2-AdSAT is in $\mathbf{P}$, and then
study two stochastic algorithms (simulated annealing and its improved variant)
and compare their performances in detail for 3-AdSAT. Varying the density of
clauses $\alpha$ we find a sharp SAT-UNSAT transition at a critical value whose
upper bound is $\alpha_c \lesssim 1.5$, thus providing a much stricter upper
bound for the QSAT transition than those previously found.
Leveraging Long-Term Predictions and Online-Learning in Agent-based Multiple Person Tracking
Authors: Wenxi Liu, Antoni B. Chan, Rynson W. H. Lau, Dinesh Manocha
Date: 2014-02-10 00:00:00
Tasks: Position

We present a multiple-person tracking algorithm, based on combining particle
filters and RVO, an agent-based crowd model that infers collision-free
velocities so as to predict pedestrian's motion. In addition to position and
velocity, our tracking algorithm can estimate the internal goals (desired
destination or desired velocity) of the tracked pedestrian in an online manner,
thus removing the need to specify this information beforehand. Furthermore, we
leverage the longer-term predictions of RVO by deriving a higher-order particle
filter, which aggregates multiple predictions from different prior time steps.
This yields a tracker that can recover from short-term occlusions and spurious
noise in the appearance model. Experimental results show that our tracking
algorithm is suitable for predicting pedestrians' behaviors online without
needing scene priors or hand-annotated goal information, and improves tracking
in real-world crowded scenes under low frame rates.
Finding Eyewitness Tweets During Crises
Authors: Fred Morstatter, Nichola Lubold, Heather Pon-Barry, Jürgen Pfeffer, Huan Liu
Date: 2014-03-07 00:00:00
Proceeding: WS 2014 6
Tasks: Disaster Response

Disaster response agencies have started to incorporate social media as a
source of fast-breaking information to understand the needs of people affected
by the many crises that occur around the world. These agencies look for tweets
from within the region affected by the crisis to get the latest updates of the
status of the affected region. However only 1% of all tweets are geotagged with
explicit location information. First responders lose valuable information
because they cannot assess the origin of many of the tweets they collect. In
this work we seek to identify non-geotagged tweets that originate from within
the crisis region. Towards this, we address three questions: (1) is there a
difference between the language of tweets originating within a crisis region
and tweets originating outside the region, (2) what are the linguistic patterns
that can be used to differentiate within-region and outside-region tweets, and
(3) for non-geotagged tweets, can we automatically identify those originating
within the crisis region in real-time?
Ant Colony based Feature Selection Heuristics for Retinal Vessel Segmentation
Authors: Ahmed. H. Asad, Ahmad Taher Azar, Nashwa El-Bendary, Aboul Ella Hassaanien
Date: 2014-03-07 00:00:00
Tasks: feature selection, General Classification, Retinal Vessel Segmentation, Specificity

Features selection is an essential step for successful data classification,
since it reduces the data dimensionality by removing redundant features.
Consequently, that minimizes the classification complexity and time in addition
to maximizing its accuracy. In this article, a comparative study considering
six features selection heuristics is conducted in order to select the best
relevant features subset. The tested features vector consists of fourteen
features that are computed for each pixel in the field of view of retinal
images in the DRIVE database. The comparison is assessed in terms of
sensitivity, specificity, and accuracy measurements of the recommended features
subset resulted by each heuristic when applied with the ant colony system.
Experimental results indicated that the features subset recommended by the
relief heuristic outperformed the subsets recommended by the other experienced
heuristics.
Recycling Proof Patterns in Coq: Case Studies
Authors: Jónathan Heras, Ekaterina Komendantskaya
Date: 2013-01-25 00:00:00
Tasks: BIG-bench Machine Learning

Development of Interactive Theorem Provers has led to the creation of big
libraries and varied infrastructures for formal proofs. However, despite (or
perhaps due to) their sophistication, the re-use of libraries by non-experts or
across domains is a challenge. In this paper, we provide detailed case studies
and evaluate the machine-learning tool ML4PG built to interactively data-mine
the electronic libraries of proofs, and to provide user guidance on the basis
of proof patterns found in the existing libraries.
Continuous Features Discretization for Anomaly Intrusion Detectors Generation
Authors: Amira Sayed A. Aziz, Ahmad Taher Azar, Aboul Ella Hassanien, Sanaa Al-Ola Hanafy
Date: 2014-03-07 00:00:00
Tasks: Intrusion Detection

Network security is a growing issue, with the evolution of computer systems
and expansion of attacks. Biological systems have been inspiring scientists and
designs for new adaptive solutions, such as genetic algorithms. In this paper,
we present an approach that uses the genetic algorithm to generate anomaly net-
work intrusion detectors. In this paper, an algorithm propose use a
discretization method for the continuous features selected for the intrusion
detection, to create some homogeneity between values, which have different data
types. Then,the intrusion detection system is tested against the NSL-KDD data
set using different distance methods. A comparison is held amongst the results,
and it is shown by the end that this proposed approach has good results, and
recommendations is given for future experiments.
On the Sequence of State Configurations in the Garden of Eden
Authors: Yukihiro Kamada, Kiyonori Miyasaki
Date: 2014-03-07 00:00:00

Autonomous threshold element circuit networks are used to investigate the
structure of neural networks. With these circuits, as the transition functions
are threshold functions, it is necessary to consider the existence of sequences
of state configurations that cannot be transitioned. In this study, we focus on
all logical functions of four or fewer variables, and we discuss the periodic
sequences and transient series that transition from all sequences of state
configurations. Furthermore, by using the sequences of state configurations in
the Garden of Eden, we show that it is easy to obtain functions that determine
the operation of circuit networks.
The Why and How of Nonnegative Matrix Factorization
Authors: Nicolas Gillis
Date: 2014-01-21 00:00:00

Nonnegative matrix factorization (NMF) has become a widely used tool for the
analysis of high-dimensional data as it automatically extracts sparse and
meaningful features from a set of nonnegative data vectors. We first illustrate
this property of NMF on three applications, in image processing, text mining
and hyperspectral imaging --this is the why. Then we address the problem of
solving NMF, which is NP-hard in general. We review some standard NMF
algorithms, and also present a recent subclass of NMF problems, referred to as
near-separable NMF, that can be solved efficiently (that is, in polynomial
time), even in the presence of noise --this is the how. Finally, we briefly
describe some problems in mathematics and computer science closely related to
NMF via the nonnegative rank.
Compressive Hyperspectral Imaging Using Progressive Total Variation
Authors: Simeon Kamdem Kuiteing, Giulio Coluccia, Alessandro Barducci, Mauro Barni, Enrico Magli
Date: 2014-03-07 00:00:00
Tasks: compressed sensing, Earth Observation

Compressed Sensing (CS) is suitable for remote acquisition of hyperspectral
images for earth observation, since it could exploit the strong spatial and
spectral correlations, llowing to simplify the architecture of the onboard
sensors. Solutions proposed so far tend to decouple spatial and spectral
dimensions to reduce the complexity of the reconstruction, not taking into
account that onboard sensors progressively acquire spectral rows rather than
acquiring spectral channels. For this reason, we propose a novel progressive CS
architecture based on separate sensing of spectral rows and joint
reconstruction employing Total Variation. Experimental results run on raw
AVIRIS and AIRS images confirm the validity of the proposed system.
Rigid-Motion Scattering for Texture Classification
Authors: Laurent SIfre, Stéphane Mallat
Date: 2014-03-07 00:00:00
Tasks: Classification, General Classification, Texture Classification, Translation

A rigid-motion scattering computes adaptive invariants along translations and
rotations, with a deep convolutional network. Convolutions are calculated on
the rigid-motion group, with wavelets defined on the translation and rotation
variables. It preserves joint rotation and translation information, while
providing global invariants at any desired scale. Texture classification is
studied, through the characterization of stationary processes from a single
realization. State-of-the-art results are obtained on multiple texture data
bases, with important rotation and scaling variabilities.
Feature Extraction of ECG Signal Using HHT Algorithm
Authors: Neha Soorma, Jaikaran Singh, Mukesh Tiwari
Date: 2014-03-07 00:00:00
Tasks: Anomaly Detection, Prognosis

This paper describe the features extraction algorithm for electrocardiogram
(ECG) signal using Huang Hilbert Transform and Wavelet Transform. ECG signal
for an individual human being is different due to unique heart structure. The
purpose of feature extraction of ECG signal would allow successful abnormality
detection and efficient prognosis due to heart disorder. Some major important
features will be extracted from ECG signals such as amplitude, duration,
pre-gradient, post-gradient and so on. Therefore, we need a strong mathematical
model to extract such useful parameter. Here an adaptive mathematical analysis
model is Hilbert-Huang transform (HHT). This new approach, the Hilbert-Huang
transform, is implemented to analyze the non-linear and nonstationary data. It
is unique and different from the existing methods of data analysis and does not
require an a priori functional basis. The effectiveness of the proposed scheme
is verified through the simulation.