7,526 research outputs found
Online Anomaly Detection via Class-Imbalance Learning
Anomaly detection is an important task in many real world applications such
as fraud detection, suspicious activity detection, health care monitoring etc.
In this paper, we tackle this problem from supervised learning perspective in
online learning setting. We maximize well known \emph{Gmean} metric for
class-imbalance learning in online learning framework. Specifically, we show
that maximizing \emph{Gmean} is equivalent to minimizing a convex surrogate
loss function and based on that we propose novel online learning algorithm for
anomaly detection. We then show, by extensive experiments, that the performance
of the proposed algorithm with respect to metric is as good as a recently
proposed Cost-Sensitive Online Classification(CSOC) algorithm for
class-imbalance learning over various benchmarked data sets while keeping
running time close to the perception algorithm. Our another conclusion is that
other competitive online algorithms do not perform consistently over data sets
of varying size. This shows the potential applicability of our proposed
approach.Comment: This paper is accepted for publication in IC3 2015, Jaypee Noid
Evaluation of a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery
X-ray baggage security screening is widely used to maintain aviation and
transport security. Of particular interest is the focus on automated security
X-ray analysis for particular classes of object such as electronics, electrical
items, and liquids. However, manual inspection of such items is challenging
when dealing with potentially anomalous items. Here we present a dual
convolutional neural network (CNN) architecture for automatic anomaly detection
within complex security X-ray imagery. We leverage recent advances in
region-based (R-CNN), mask-based CNN (Mask R-CNN) and detection architectures
such as RetinaNet to provide object localisation variants for specific object
classes of interest. Subsequently, leveraging a range of established CNN object
and fine-grained category classification approaches we formulate within object
anomaly detection as a two-class problem (anomalous or benign). While the best
performing object localisation method is able to perform with 97.9% mean
average precision (mAP) over a six-class X-ray object detection problem,
subsequent two-class anomaly/benign classification is able to achieve 66%
performance for within object anomaly detection. Overall, this performance
illustrates both the challenge and promise of object-wise anomaly detection
within the context of cluttered X-ray security imagery.Comment: IJCNN 201
A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised
Credit card has become popular mode of payment for both online and offline
purchase, which leads to increasing daily fraud transactions. An Efficient
fraud detection methodology is therefore essential to maintain the reliability
of the payment system. In this study, we perform a comparison study of credit
card fraud detection by using various supervised and unsupervised approaches.
Specifically, 6 supervised classification models, i.e., Logistic Regression
(LR), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Tree
(DT), Random Forest (RF), Extreme Gradient Boosting (XGB), as well as 4
unsupervised anomaly detection models, i.e., One-Class SVM (OCSVM),
Auto-Encoder (AE), Restricted Boltzmann Machine (RBM), and Generative
Adversarial Networks (GAN), are explored in this study. We train all these
models on a public credit card transaction dataset from Kaggle website, which
contains 492 frauds out of 284,807 transactions. The labels of the transactions
are used for supervised learning models only. The performance of each model is
evaluated through 5-fold cross validation in terms of Area Under the Receiver
Operating Curves (AUROC). Within supervised approaches, XGB and RF obtain the
best performance with AUROC = 0.989 and AUROC = 0.988, respectively. While for
unsupervised approaches, RBM achieves the best performance with AUROC = 0.961,
followed by GAN with AUROC = 0.954. The experimental results show that
supervised models perform slightly better than unsupervised models in this
study. Anyway, unsupervised approaches are still promising for credit card
fraud transaction detection due to the insufficient annotation and the data
imbalance issue in real-world applications
Feedforward Neural Network for Time Series Anomaly Detection
Time series anomaly detection is usually formulated as finding outlier data
points relative to some usual data, which is also an important problem in
industry and academia. To ensure systems working stably, internet companies,
banks and other companies need to monitor time series, which is called KPI (Key
Performance Indicators), such as CPU used, number of orders, number of online
users and so on. However, millions of time series have several shapes (e.g.
seasonal KPIs, KPIs of timed tasks and KPIs of CPU used), so that it is very
difficult to use a simple statistical model to detect anomaly for all kinds of
time series. Although some anomaly detectors have developed many years and some
supervised models are also available in this field, we find many methods have
their own disadvantages. In this paper, we present our system, which is based
on deep feedforward neural network and detect anomaly points of time series.
The main difference between our system and other systems based on supervised
models is that we do not need feature engineering of time series to train deep
feedforward neural network in our system, which is essentially an end-to-end
system
Anomaly Detection in Images
Visual defect assessment is a form of anomaly detection. This is very
relevant in finding faults such as cracks and markings in various surface
inspection tasks like pavement and automotive parts. The task involves
detection of deviation/divergence of anomalous samples from the normal ones.
Two of the major challenges in supervised anomaly detection are the lack of
labelled training data and the low availability of anomaly instances.
Semi-supervised methods which learn the underlying distribution of the normal
samples and then measure the deviation/divergence from the estimated model as
the anomaly score have limitations in their overall ability to detect
anomalies. This paper proposes the application of network-based deep transfer
learning using convolutional neural networks (CNNs) for the task of anomaly
detection. Single class SVMs have been used in the past with some success,
however we hypothesize that deeper networks for single class classification
should perform better. Results obtained on established anomaly detection
benchmarks as well as on a real-world dataset, show that the proposed method
clearly outperforms the existing state-of-the-art methods, by achieving a
staggering average area under the receiver operating characteristic curve value
of 0.99 for the tested data-sets which is an average improvement of 41% on the
CIFAR10, 20% on MNIST and 16% on Cement Crack data-sets
A Framework of Sparse Online Learning and Its Applications
The amount of data in our society has been exploding in the era of big data
today. In this paper, we address several open challenges of big data stream
classification, including high volume, high velocity, high dimensionality, high
sparsity, and high class-imbalance. Many existing studies in data mining
literature solve data stream classification tasks in a batch learning setting,
which suffers from poor efficiency and scalability when dealing with big data.
To overcome the limitations, this paper investigates an online learning
framework for big data stream classification tasks. Unlike some existing online
data stream classification techniques that are often based on first-order
online learning, we propose a framework of Sparse Online Classification (SOC)
for data stream classification, which includes some state-of-the-art
first-order sparse online learning algorithms as special cases and allows us to
derive a new effective second-order online learning algorithm for data stream
classification. In addition, we also propose a new cost-sensitive sparse online
learning algorithm by extending the framework with application to tackle online
anomaly detection tasks where class distribution of data could be very
imbalanced. We also analyze the theoretical bounds of the proposed method, and
finally conduct an extensive set of experiments, in which encouraging results
validate the efficacy of the proposed algorithms in comparison to a family of
state-of-the-art techniques on a variety of data stream classification tasks.Comment: 13 pages, 14 figure
Usage of multiple RTL features for Earthquake prediction
We construct a classification model that predicts if an earthquake with the
magnitude above a threshold will take place at a given location in a time range
30-180 days from a given moment of time. A common approach is to use expert
forecasts based on features like Region-Time-Length (RTL) characteristics. The
proposed approach uses machine learning on top of multiple RTL features to take
into account effects at various scales and to improve prediction accuracy. For
historical data about Japan earthquakes 1992-2005 and predictions at locations
given in this database the best model has precision up to ~ 0.95 and recall up
to ~ 0.98.Comment: 13 pages, 3 figures, 3 table
Spatially-weighted Anomaly Detection with Regression Model
Visual anomaly detection is common in several applications including medical
screening and production quality check. Although a definition of the anomaly is
an unknown trend in data, in many cases some hints or samples of the anomaly
class can be given in advance. Conventional methods cannot use the available
anomaly data, and also do not have a robustness of noise. In this paper, we
propose a novel spatially-weighted reconstruction-loss-based anomaly detection
with a likelihood value from a regression model trained by all known data. The
spatial weights are calculated by a region of interest generated from employing
visualization of the regression model. We introduce some ways to combine with
various strategies to propose a state-of-the-art method. Comparing with other
methods on three different datasets, we empirically verify the proposed method
performs better than the others.Comment: 4 pages, published as an oral presentation paper at Meeting on Image
Recognition and Understanding (MIRU) 201
Unsupervised Synthesis of Anomalies in Videos: Transforming the Normal
Abnormal activity recognition requires detection of occurrence of anomalous
events that suffer from a severe imbalance in data. In a video, normal is used
to describe activities that conform to usual events while the irregular events
which do not conform to the normal are referred to as abnormal. It is far more
common to observe normal data than to obtain abnormal data in visual
surveillance. In this paper, we propose an approach where we can obtain
abnormal data by transforming normal data. This is a challenging task that is
solved through a multi-stage pipeline approach. We utilize a number of
techniques from unsupervised segmentation in order to synthesize new samples of
data that are transformed from an existing set of normal examples. Further,
this synthesis approach has useful applications as a data augmentation
technique. An incrementally trained Bayesian convolutional neural network (CNN)
is used to carefully select the set of abnormal samples that can be added.
Finally through this synthesis approach we obtain a comparable set of abnormal
samples that can be used for training the CNN for the classification of normal
vs abnormal samples. We show that this method generalizes to multiple settings
by evaluating it on two real world datasets and achieves improved performance
over other probabilistic techniques that have been used in the past for this
task.Comment: Accepted in IJCNN 201
MDGAN: Boosting Anomaly Detection Using \\Multi-Discriminator Generative Adversarial Networks
Anomaly detection is often considered a challenging field of machine learning
due to the difficulty of obtaining anomalous samples for training and the need
to obtain a sufficient amount of training data. In recent years, autoencoders
have been shown to be effective anomaly detectors that train only on "normal"
data. Generative adversarial networks (GANs) have been used to generate
additional training samples for classifiers, thus making them more accurate and
robust. However, in anomaly detection GANs are only used to reconstruct
existing samples rather than to generate additional ones. This stems both from
the small amount and lack of diversity of anomalous data in most domains. In
this study we propose MDGAN, a novel GAN architecture for improving anomaly
detection through the generation of additional samples. Our approach uses two
discriminators: a dense network for determining whether the generated samples
are of sufficient quality (i.e., valid) and an autoencoder that serves as an
anomaly detector. MDGAN enables us to reconcile two conflicting goals: 1)
generate high-quality samples that can fool the first discriminator, and 2)
generate samples that can eventually be effectively reconstructed by the second
discriminator, thus improving its performance. Empirical evaluation on a
diverse set of datasets demonstrates the merits of our approach
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