227,303 research outputs found
A traffic classification method using machine learning algorithm
Applying concepts of attack investigation in IT industry, this idea has been developed to design
a Traffic Classification Method using Data Mining techniques at the intersection of Machine
Learning Algorithm, Which will classify the normal and malicious traffic. This classification will
help to learn about the unknown attacks faced by IT industry. The notion of traffic classification
is not a new concept; plenty of work has been done to classify the network traffic for
heterogeneous application nowadays. Existing techniques such as (payload based, port based
and statistical based) have their own pros and cons which will be discussed in this
literature later, but classification using Machine Learning techniques is still an open field to explore and has provided very promising results up till now
Application of mutual information-based sequential feature selection to ISBSG mixed data
[EN] There is still little research work focused on feature selection (FS) techniques including both categorical and continuous features in Software Development Effort Estimation (SDEE) literature. This paper addresses the problem of selecting the most relevant features from ISBSG (International Software Benchmarking Standards Group) dataset to be used in SDEE. The aim is to show the usefulness of splitting the ranked list of features provided by a mutual information-based sequential FS approach in two, regarding categorical and continuous features. These lists are later recombined according to the accuracy of a case-based reasoning model. Thus, four FS algorithms are compared using a complete dataset with 621 projects and 12 features from ISBSG. On the one hand, two algorithms just consider the relevance, while the remaining two follow the criterion of maximizing relevance and also minimizing redundancy between any independent feature and the already selected features. On the other hand, the algorithms that do not discriminate between continuous and categorical features consider just one list, whereas those that differentiate them use two lists that are later combined. As a result, the algorithms that use two lists present better performance than those algorithms that use one list. Thus, it is meaningful to consider two different lists of features so that the categorical features may be selected more frequently. We also suggest promoting the usage of Application Group, Project Elapsed Time, and First Data Base System features with preference over the more frequently used Development Type, Language Type, and Development Platform.FernĂĄndez-Diego, M.; GonzĂĄlez-LadrĂłn-De-Guevara, F. (2018). Application of mutual information-based sequential feature selection to ISBSG mixed data. Software Quality Journal. 26(4):1299-1325. https://doi.org/10.1007/s11219-017-9391-5S12991325264Angelis, L., & Stamelos, I. (2000). 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Presented at the 2010 10th IEEE/IPSJ International Symposium on Applications and the Internet (SAINT). https://doi.org/10.1109/SAINT.2010.50 .Witten, I.H., Frank, E., Hall, M.A., Pal, C.J. (2011). Data mining: Practical machine learning tools and techniques. Morgan Kaufmann
Bandwidth selection for kernel estimation in mixed multi-dimensional spaces
Kernel estimation techniques, such as mean shift, suffer from one major
drawback: the kernel bandwidth selection. The bandwidth can be fixed for all
the data set or can vary at each points. Automatic bandwidth selection becomes
a real challenge in case of multidimensional heterogeneous features. This paper
presents a solution to this problem. It is an extension of \cite{Comaniciu03a}
which was based on the fundamental property of normal distributions regarding
the bias of the normalized density gradient. The selection is done iteratively
for each type of features, by looking for the stability of local bandwidth
estimates across a predefined range of bandwidths. A pseudo balloon mean shift
filtering and partitioning are introduced. The validity of the method is
demonstrated in the context of color image segmentation based on a
5-dimensional space
Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations
Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions
Inducing Features of Random Fields
We present a technique for constructing random fields from a set of training
samples. The learning paradigm builds increasingly complex fields by allowing
potential functions, or features, that are supported by increasingly large
subgraphs. Each feature has a weight that is trained by minimizing the
Kullback-Leibler divergence between the model and the empirical distribution of
the training data. A greedy algorithm determines how features are incrementally
added to the field and an iterative scaling algorithm is used to estimate the
optimal values of the weights.
The statistical modeling techniques introduced in this paper differ from
those common to much of the natural language processing literature since there
is no probabilistic finite state or push-down automaton on which the model is
built. Our approach also differs from the techniques common to the computer
vision literature in that the underlying random fields are non-Markovian and
have a large number of parameters that must be estimated. Relations to other
learning approaches including decision trees and Boltzmann machines are given.
As a demonstration of the method, we describe its application to the problem of
automatic word classification in natural language processing.
Key words: random field, Kullback-Leibler divergence, iterative scaling,
divergence geometry, maximum entropy, EM algorithm, statistical learning,
clustering, word morphology, natural language processingComment: 34 pages, compressed postscrip
Positive Data Clustering based on Generalized Inverted Dirichlet Mixture Model
Recent advances in processing and networking capabilities of computers have caused an accumulation
of immense amounts of multimodal multimedia data (image, text, video). These data
are generally presented as high-dimensional vectors of features. The availability of these highdimensional
data sets has provided the input to a large variety of statistical learning applications
including clustering, classification, feature selection, outlier detection and density estimation. In
this context, a finite mixture offers a formal approach to clustering and a powerful tool to tackle
the problem of data modeling. A mixture model assumes that the data is generated by a set of
parametric probability distributions. The main learning process of a mixture model consists of the
following two parts: parameter estimation and model selection (estimation the number of components).
In addition, other issues may be considered during the learning process of mixture models
such as the: a) feature selection and b) outlier detection. The main objective of this thesis is to
work with different kinds of estimation criteria and to incorporate those challenges into a single
framework.
The first contribution of this thesis is to propose a statistical framework which can tackle the problem
of parameter estimation, model selection, feature selection, and outlier rejection in a unified
model. We propose to use feature saliency and introduce an expectation-maximization (EM) algorithm
for the estimation of the Generalized Inverted Dirichlet (GID) mixture model. By using
the Minimum Message Length (MML), we can identify how much each feature contributes to
our model as well as determine the number of components. The presence of outliers is an added
challenge and is handled by incorporating an auxiliary outlier component, to which we associate a uniform density. Experimental results on synthetic data, as well as real world applications involving
visual scenes and object classification, indicates that the proposed approach was promising,
even though low-dimensional representation of the data was applied. In addition, it showed
the importance of embedding an outlier component to the proposed model. EM learning suffers
from significant drawbacks. In order to overcome those drawbacks, a learning approach using a
Bayesian framework is proposed as our second contribution. This learning is based on the estimation
of the parameters posteriors and by considering the prior knowledge about these parameters.
Calculation of the posterior distribution of each parameter in the model is done by using Markov
chain Monte Carlo (MCMC) simulation methods - namely, the Gibbs sampling and the Metropolis-
Hastings methods. The Bayesian Information Criterion (BIC) was used for model selection. The
proposed model was validated on object classification and forgery detection applications. For the
first two contributions, we developed a finite GID mixture. However, in the third contribution,
we propose an infinite GID mixture model. The proposed model simutaneously tackles the clustering
and feature selection problems. The proposed learning model is based on Gibbs sampling.
The effectiveness of the proposed method is shown using image categorization application. Our
last contribution in this thesis is another fully Bayesian approach for a finite GID mixture learning
model using the Reversible Jump Markov Chain Monte Carlo (RJMCMC) technique. The
proposed algorithm allows for the simultaneously handling of the model selection and parameter estimation for high dimensional data. The merits of this approach are investigated using synthetic
data, and data generated from a challenging namely object detection
A New Estimator of Intrinsic Dimension Based on the Multipoint Morisita Index
The size of datasets has been increasing rapidly both in terms of number of
variables and number of events. As a result, the empty space phenomenon and the
curse of dimensionality complicate the extraction of useful information. But,
in general, data lie on non-linear manifolds of much lower dimension than that
of the spaces in which they are embedded. In many pattern recognition tasks,
learning these manifolds is a key issue and it requires the knowledge of their
true intrinsic dimension. This paper introduces a new estimator of intrinsic
dimension based on the multipoint Morisita index. It is applied to both
synthetic and real datasets of varying complexities and comparisons with other
existing estimators are carried out. The proposed estimator turns out to be
fairly robust to sample size and noise, unaffected by edge effects, able to
handle large datasets and computationally efficient
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