56,013 research outputs found
BMC Bioinformatics
BackgroundSuccessfully modeling high-dimensional data involving thousands of variables is challenging. This is especially true for gene expression profiling experiments, given the large number of genes involved and the small number of samples available. Random Forests (RF) is a popular and widely used approach to feature selection for such "small n, large p problems." However, Random Forests suffers from instability, especially in the presence of noisy and/or unbalanced inputs.ResultsWe present RKNN-FS, an innovative feature selection procedure for "small n, large p problems." RKNN-FS is based on Random KNN (RKNN), a novel generalization of traditional nearest-neighbor modeling. RKNN consists of an ensemble of base k-nearest neighbor models, each constructed from a random subset of the input variables. To rank the importance of the variables, we define a criterion on the RKNN framework, using the notion of support. A two-stage backward model selection method is then developed based on this criterion. Empirical results on microarray data sets with thousands of variables and relatively few samples show that RKNN-FS is an effective feature selection approach for high-dimensional data. RKNN is similar to Random Forests in terms of classification accuracy without feature selection. However, RKNN provides much better classification accuracy than RF when each method incorporates a feature-selection step. Our results show that RKNN is significantly more stable and more robust than Random Forests for feature selection when the input data are noisy and/or unbalanced. Further, RKNN-FS is much faster than the Random Forests feature selection method (RF-FS), especially for large scale problems, involving thousands of variables and multiple classes.ConclusionsGiven the superiority of Random KNN in classification performance when compared with Random Forests, RKNN-FS's simplicity and ease of implementation, and its superiority in speed and stability, we propose RKNN-FS as a faster and more stable alternative to Random Forests in classification problems involving feature selection for high-dimensional datasets
Stable Feature Selection for Biomarker Discovery
Feature selection techniques have been used as the workhorse in biomarker
discovery applications for a long time. Surprisingly, the stability of feature
selection with respect to sampling variations has long been under-considered.
It is only until recently that this issue has received more and more attention.
In this article, we review existing stable feature selection methods for
biomarker discovery using a generic hierarchal framework. We have two
objectives: (1) providing an overview on this new yet fast growing topic for a
convenient reference; (2) categorizing existing methods under an expandable
framework for future research and development
A Nonparametric Ensemble Binary Classifier and its Statistical Properties
In this work, we propose an ensemble of classification trees (CT) and
artificial neural networks (ANN). Several statistical properties including
universal consistency and upper bound of an important parameter of the proposed
classifier are shown. Numerical evidence is also provided using various real
life data sets to assess the performance of the model. Our proposed
nonparametric ensemble classifier doesn't suffer from the `curse of
dimensionality' and can be used in a wide variety of feature selection cum
classification problems. Performance of the proposed model is quite better when
compared to many other state-of-the-art models used for similar situations
The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures
Motivation: Biomarker discovery from high-dimensional data is a crucial
problem with enormous applications in biology and medicine. It is also
extremely challenging from a statistical viewpoint, but surprisingly few
studies have investigated the relative strengths and weaknesses of the plethora
of existing feature selection methods. Methods: We compare 32 feature selection
methods on 4 public gene expression datasets for breast cancer prognosis, in
terms of predictive performance, stability and functional interpretability of
the signatures they produce. Results: We observe that the feature selection
method has a significant influence on the accuracy, stability and
interpretability of signatures. Simple filter methods generally outperform more
complex embedded or wrapper methods, and ensemble feature selection has
generally no positive effect. Overall a simple Student's t-test seems to
provide the best results. Availability: Code and data are publicly available at
http://cbio.ensmp.fr/~ahaury/
Asymmetric Pruning for Learning Cascade Detectors
Cascade classifiers are one of the most important contributions to real-time
object detection. Nonetheless, there are many challenging problems arising in
training cascade detectors. One common issue is that the node classifier is
trained with a symmetric classifier. Having a low misclassification error rate
does not guarantee an optimal node learning goal in cascade classifiers, i.e.,
an extremely high detection rate with a moderate false positive rate. In this
work, we present a new approach to train an effective node classifier in a
cascade detector. The algorithm is based on two key observations: 1) Redundant
weak classifiers can be safely discarded; 2) The final detector should satisfy
the asymmetric learning objective of the cascade architecture. To achieve this,
we separate the classifier training into two steps: finding a pool of
discriminative weak classifiers/features and training the final classifier by
pruning weak classifiers which contribute little to the asymmetric learning
criterion (asymmetric classifier construction). Our model reduction approach
helps accelerate the learning time while achieving the pre-determined learning
objective. Experimental results on both face and car data sets verify the
effectiveness of the proposed algorithm. On the FDDB face data sets, our
approach achieves the state-of-the-art performance, which demonstrates the
advantage of our approach.Comment: 14 page
Dissimilarity-based Ensembles for Multiple Instance Learning
In multiple instance learning, objects are sets (bags) of feature vectors
(instances) rather than individual feature vectors. In this paper we address
the problem of how these bags can best be represented. Two standard approaches
are to use (dis)similarities between bags and prototype bags, or between bags
and prototype instances. The first approach results in a relatively
low-dimensional representation determined by the number of training bags, while
the second approach results in a relatively high-dimensional representation,
determined by the total number of instances in the training set. In this paper
a third, intermediate approach is proposed, which links the two approaches and
combines their strengths. Our classifier is inspired by a random subspace
ensemble, and considers subspaces of the dissimilarity space, defined by
subsets of instances, as prototypes. We provide guidelines for using such an
ensemble, and show state-of-the-art performances on a range of multiple
instance learning problems.Comment: Submitted to IEEE Transactions on Neural Networks and Learning
Systems, Special Issue on Learning in Non-(geo)metric Space
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
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