137 research outputs found
Feature selection in high-dimensional dataset using MapReduce
This paper describes a distributed MapReduce implementation of the minimum
Redundancy Maximum Relevance algorithm, a popular feature selection method in
bioinformatics and network inference problems. The proposed approach handles
both tall/narrow and wide/short datasets. We further provide an open source
implementation based on Hadoop/Spark, and illustrate its scalability on
datasets involving millions of observations or features
Distributed Correlation-Based Feature Selection in Spark
CFS (Correlation-Based Feature Selection) is an FS algorithm that has been
successfully applied to classification problems in many domains. We describe
Distributed CFS (DiCFS) as a completely redesigned, scalable, parallel and
distributed version of the CFS algorithm, capable of dealing with the large
volumes of data typical of big data applications. Two versions of the algorithm
were implemented and compared using the Apache Spark cluster computing model,
currently gaining popularity due to its much faster processing times than
Hadoop's MapReduce model. We tested our algorithms on four publicly available
datasets, each consisting of a large number of instances and two also
consisting of a large number of features. The results show that our algorithms
were superior in terms of both time-efficiency and scalability. In leveraging a
computer cluster, they were able to handle larger datasets than the
non-distributed WEKA version while maintaining the quality of the results,
i.e., exactly the same features were returned by our algorithms when compared
to the original algorithm available in WEKA.Comment: 25 pages, 5 figure
Effective Discriminative Feature Selection with Non-trivial Solutions
Feature selection and feature transformation, the two main ways to reduce
dimensionality, are often presented separately. In this paper, a feature
selection method is proposed by combining the popular transformation based
dimensionality reduction method Linear Discriminant Analysis (LDA) and sparsity
regularization. We impose row sparsity on the transformation matrix of LDA
through -norm regularization to achieve feature selection, and
the resultant formulation optimizes for selecting the most discriminative
features and removing the redundant ones simultaneously. The formulation is
extended to the -norm regularized case: which is more likely to
offer better sparsity when . Thus the formulation is a better
approximation to the feature selection problem. An efficient algorithm is
developed to solve the -norm based optimization problem and it is
proved that the algorithm converges when . Systematical experiments
are conducted to understand the work of the proposed method. Promising
experimental results on various types of real-world data sets demonstrate the
effectiveness of our algorithm
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
High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso
The goal of supervised feature selection is to find a subset of input
features that are responsible for predicting output values. The least absolute
shrinkage and selection operator (Lasso) allows computationally efficient
feature selection based on linear dependency between input features and output
values. In this paper, we consider a feature-wise kernelized Lasso for
capturing non-linear input-output dependency. We first show that, with
particular choices of kernel functions, non-redundant features with strong
statistical dependence on output values can be found in terms of kernel-based
independence measures. We then show that the globally optimal solution can be
efficiently computed; this makes the approach scalable to high-dimensional
problems. The effectiveness of the proposed method is demonstrated through
feature selection experiments with thousands of features.Comment: 18 page
An Improved Parallelized mRMR for Gene Subset Selection in Cancer Classification
DNA microarray technique has become a more attractive tool for cancer classification in the scientific and industrial fields. Based on the previous researchers, the conventional approach for cancer classification is primarily based on morphological appearance of the tumor. The limitations of this approach are bias in identify the tumors by expert and faced the difficulty in differentiate the cancer subtypes due to most cancers being highly related to the specific biological insight. Thus, this study propose an improved parallelized Minimum Redundancy Maximum Relevance (mRMR), which is a particularly fast feature selection method for finding a set of both relevant and complementary features. The mRMR can identify genes more relevance to biological context that leads to richer biological interpretations. The proposed method is expected to achieve accurate classification performance using small number of predictive genes when tested using two datasets from Cancer Genome Project and compared to previous methods
Embedding Feature Selection for Large-scale Hierarchical Classification
Large-scale Hierarchical Classification (HC) involves datasets consisting of
thousands of classes and millions of training instances with high-dimensional
features posing several big data challenges. Feature selection that aims to
select the subset of discriminant features is an effective strategy to deal
with large-scale HC problem. It speeds up the training process, reduces the
prediction time and minimizes the memory requirements by compressing the total
size of learned model weight vectors. Majority of the studies have also shown
feature selection to be competent and successful in improving the
classification accuracy by removing irrelevant features. In this work, we
investigate various filter-based feature selection methods for dimensionality
reduction to solve the large-scale HC problem. Our experimental evaluation on
text and image datasets with varying distribution of features, classes and
instances shows upto 3x order of speed-up on massive datasets and upto 45% less
memory requirements for storing the weight vectors of learned model without any
significant loss (improvement for some datasets) in the classification
accuracy. Source Code: https://cs.gmu.edu/~mlbio/featureselection.Comment: IEEE International Conference on Big Data (IEEE BigData 2016
Parallel feature selection for distributed-memory clusters
Versión final aceptada de: https://doi.org/10.1016/j.ins.2019.01.050This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/
licenses/by-nc-nd/4.0/. This version of the article: González-DomÃnguez, J. et al. (2019) ‘Parallel feature selection for
distributed-memory clusters’, has been accepted for publication in Information Sciences, 496, pp. 399–409. The
Version of Record is available online at: https://doi.org/10.1016/j.ins.2019.01.050[Abstract]: Feature selection is nowadays an extremely important data mining stage in the field of machine learning due to the appearance of problems of high dimensionality. In the literature there are numerous feature selection methods, mRMR (minimum-Redundancy-Maximum-Relevance) being one of the most widely used. However, although it achieves good results in selecting relevant features, it is impractical for datasets with thousands of features. A possible solution to this limitation is the use of the fast-mRMR method, a greedy optimization of the mRMR algorithm that improves both scalability and efficiency. In this work we present fast-mRMR-MPI, a novel hybrid parallel implementation that uses MPI and OpenMP to accelerate feature selection on distributed-memory clusters. Our performance evaluation on two different systems using five representative input datasets shows that fast-mRMR-MPI is significantly faster than fast-mRMR while providing the same results. As an example, our tool needs less than one minute to select 200 features of a dataset with more than four million features and 16,000 samples on a cluster with 32 nodes (768 cores in total), while the sequential fast-mRMR required more than eight hours. Moreover, fast-mRMR-MPI distributes data so that it is able to exploit the memory available on different nodes of a cluster and then complete analyses that fail on a single node due to memory constraints. Our tool is publicly available at https://github.com/borjaf696/Fast-mRMR.This research has been partially funded by projects TIN2016-75845-P and TIN-2015-65069-C2-1-R of the Ministry of Economy, Industry and Competitiveness of Spain, as well as by Xunta de Galicia projects ED431D R2016/045 and GRC2014/035, all of them partially funded by FEDER funds of the European Union. We gratefully thank CESGA for providing access to the Finis Terrae II supercomputer.Xunta de Galicia; ED431D R2016/045Xunta de Galicia; GRC2014/03
Optimal Search Based Gene Selection for Cancer Prognosis
Gene array data have been widely used for cancer diagnosis in recent years. However, high dimensionality has been a major problem for gene array-based classification. Gene selection is critical for accurate classification and for identifying the marker genes to discriminate different tumor types. This paper created a framework of gene selection methods based on previous studies. We focused on optimal search-based gene subset selection methods that evaluate the group performance of genes and help to pinpoint global optimal set of marker genes. Notably, this study is the first to introduce tabu search to gene selection from high dimensional gene array data. Experimental studies on several gene array datasets demonstrated the effectiveness of optimal search-based gene subset selection to identify marker genes
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