24,882 research outputs found
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
Universal microscopic correlation functions for products of independent Ginibre matrices
We consider the product of n complex non-Hermitian, independent random
matrices, each of size NxN with independent identically distributed Gaussian
entries (Ginibre matrices). The joint probability distribution of the complex
eigenvalues of the product matrix is found to be given by a determinantal point
process as in the case of a single Ginibre matrix, but with a more complicated
weight given by a Meijer G-function depending on n. Using the method of
orthogonal polynomials we compute all eigenvalue density correlation functions
exactly for finite N and fixed n. They are given by the determinant of the
corresponding kernel which we construct explicitly. In the large-N limit at
fixed n we first determine the microscopic correlation functions in the bulk
and at the edge of the spectrum. After unfolding they are identical to that of
the Ginibre ensemble with n=1 and thus universal. In contrast the microscopic
correlations we find at the origin differ for each n>1 and generalise the known
Bessel-law in the complex plane for n=2 to a new hypergeometric kernel 0_F_n-1.Comment: 20 pages, v2 published version: typos corrected and references adde
Evolving Ensemble Fuzzy Classifier
The concept of ensemble learning offers a promising avenue in learning from
data streams under complex environments because it addresses the bias and
variance dilemma better than its single model counterpart and features a
reconfigurable structure, which is well suited to the given context. While
various extensions of ensemble learning for mining non-stationary data streams
can be found in the literature, most of them are crafted under a static base
classifier and revisits preceding samples in the sliding window for a
retraining step. This feature causes computationally prohibitive complexity and
is not flexible enough to cope with rapidly changing environments. Their
complexities are often demanding because it involves a large collection of
offline classifiers due to the absence of structural complexities reduction
mechanisms and lack of an online feature selection mechanism. A novel evolving
ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in
this paper. pENsemble differs from existing architectures in the fact that it
is built upon an evolving classifier from data streams, termed Parsimonious
Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism,
which estimates a localized generalization error of a base classifier. A
dynamic online feature selection scenario is integrated into the pENsemble.
This method allows for dynamic selection and deselection of input features on
the fly. pENsemble adopts a dynamic ensemble structure to output a final
classification decision where it features a novel drift detection scenario to
grow the ensemble structure. The efficacy of the pENsemble has been numerically
demonstrated through rigorous numerical studies with dynamic and evolving data
streams where it delivers the most encouraging performance in attaining a
tradeoff between accuracy and complexity.Comment: this paper has been published by IEEE Transactions on Fuzzy System
Online Tool Condition Monitoring Based on Parsimonious Ensemble+
Accurate diagnosis of tool wear in metal turning process remains an open
challenge for both scientists and industrial practitioners because of
inhomogeneities in workpiece material, nonstationary machining settings to suit
production requirements, and nonlinear relations between measured variables and
tool wear. Common methodologies for tool condition monitoring still rely on
batch approaches which cannot cope with a fast sampling rate of metal cutting
process. Furthermore they require a retraining process to be completed from
scratch when dealing with a new set of machining parameters. This paper
presents an online tool condition monitoring approach based on Parsimonious
Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly
flexible principle where both ensemble structure and base-classifier structure
can automatically grow and shrink on the fly based on the characteristics of
data streams. Moreover, the online feature selection scenario is integrated to
actively sample relevant input attributes. The paper presents advancement of a
newly developed ensemble learning algorithm, pENsemble+, where online active
learning scenario is incorporated to reduce operator labelling effort. The
ensemble merging scenario is proposed which allows reduction of ensemble
complexity while retaining its diversity. Experimental studies utilising
real-world manufacturing data streams and comparisons with well known
algorithms were carried out. Furthermore, the efficacy of pENsemble was
examined using benchmark concept drift data streams. It has been found that
pENsemble+ incurs low structural complexity and results in a significant
reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic
Meaning of temperature in different thermostatistical ensembles
Depending on the exact experimental conditions, the thermodynamic properties
of physical systems can be related to one or more thermostatistical ensembles.
Here, we survey the notion of thermodynamic temperature in different
statistical ensembles, focusing in particular on subtleties that arise when
ensembles become non-equivalent. The 'mother' of all ensembles, the
microcanonical ensemble, uses entropy and internal energy (the most
fundamental, dynamically conserved quantity) to derive temperature as a
secondary thermodynamic variable. Over the past century, some confusion has
been caused by the fact that several competing microcanonical entropy
definitions are used in the literature, most commonly the volume and surface
entropies introduced by Gibbs. It can be proved, however, that only the volume
entropy satisfies exactly the traditional form of the laws of thermodynamics
for a broad class of physical systems, including all standard classical
Hamiltonian systems, regardless of their size. This mathematically rigorous
fact implies that negative 'absolute' temperatures and Carnot efficiencies
are not achievable within a standard thermodynamical framework. As an important
offspring of microcanonical thermostatistics, we shall briefly consider the
canonical ensemble and comment on the validity of the Boltzmann weight factor.
We conclude by addressing open mathematical problems that arise for systems
with discrete energy spectrum.Comment: 11 pages, 1 figur
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