26,472 research outputs found
EC3: Combining Clustering and Classification for Ensemble Learning
Classification and clustering algorithms have been proved to be successful
individually in different contexts. Both of them have their own advantages and
limitations. For instance, although classification algorithms are more powerful
than clustering methods in predicting class labels of objects, they do not
perform well when there is a lack of sufficient manually labeled reliable data.
On the other hand, although clustering algorithms do not produce label
information for objects, they provide supplementary constraints (e.g., if two
objects are clustered together, it is more likely that the same label is
assigned to both of them) that one can leverage for label prediction of a set
of unknown objects. Therefore, systematic utilization of both these types of
algorithms together can lead to better prediction performance. In this paper,
We propose a novel algorithm, called EC3 that merges classification and
clustering together in order to support both binary and multi-class
classification. EC3 is based on a principled combination of multiple
classification and multiple clustering methods using an optimization function.
We theoretically show the convexity and optimality of the problem and solve it
by block coordinate descent method. We additionally propose iEC3, a variant of
EC3 that handles imbalanced training data. We perform an extensive experimental
analysis by comparing EC3 and iEC3 with 14 baseline methods (7 well-known
standalone classifiers, 5 ensemble classifiers, and 2 existing methods that
merge classification and clustering) on 13 standard benchmark datasets. We show
that our methods outperform other baselines for every single dataset, achieving
at most 10% higher AUC. Moreover our methods are faster (1.21 times faster than
the best baseline), more resilient to noise and class imbalance than the best
baseline method.Comment: 14 pages, 7 figures, 11 table
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
A Survey on Soft Subspace Clustering
Subspace clustering (SC) is a promising clustering technology to identify
clusters based on their associations with subspaces in high dimensional spaces.
SC can be classified into hard subspace clustering (HSC) and soft subspace
clustering (SSC). While HSC algorithms have been extensively studied and well
accepted by the scientific community, SSC algorithms are relatively new but
gaining more attention in recent years due to better adaptability. In the
paper, a comprehensive survey on existing SSC algorithms and the recent
development are presented. The SSC algorithms are classified systematically
into three main categories, namely, conventional SSC (CSSC), independent SSC
(ISSC) and extended SSC (XSSC). The characteristics of these algorithms are
highlighted and the potential future development of SSC is also discussed.Comment: This paper has been published in Information Sciences Journal in 201
MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME
Computational experiments using spatial stochastic simulations have led to
important new biological insights, but they require specialized tools, a
complex software stack, as well as large and scalable compute and data analysis
resources due to the large computational cost associated with Monte Carlo
computational workflows. The complexity of setting up and managing a
large-scale distributed computation environment to support productive and
reproducible modeling can be prohibitive for practitioners in systems biology.
This results in a barrier to the adoption of spatial stochastic simulation
tools, effectively limiting the type of biological questions addressed by
quantitative modeling. In this paper, we present PyURDME, a new, user-friendly
spatial modeling and simulation package, and MOLNs, a cloud computing appliance
for distributed simulation of stochastic reaction-diffusion models. MOLNs is
based on IPython and provides an interactive programming platform for
development of sharable and reproducible distributed parallel computational
experiments
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
Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data
Similarity-based approaches represent a promising direction for time series
analysis. However, many such methods rely on parameter tuning, and some have
shortcomings if the time series are multivariate (MTS), due to dependencies
between attributes, or the time series contain missing data. In this paper, we
address these challenges within the powerful context of kernel methods by
proposing the robust \emph{time series cluster kernel} (TCK). The approach
taken leverages the missing data handling properties of Gaussian mixture models
(GMM) augmented with informative prior distributions. An ensemble learning
approach is exploited to ensure robustness to parameters by combining the
clustering results of many GMM to form the final kernel.
We evaluate the TCK on synthetic and real data and compare to other
state-of-the-art techniques. The experimental results demonstrate that the TCK
is robust to parameter choices, provides competitive results for MTS without
missing data and outstanding results for missing data.Comment: 23 pages, 6 figure
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