Please use this identifier to cite or link to this item:
|Title:||Biomarker Discovery Based on Large-Scale Feature Selection and MapReduce||Authors:||kourid, ahlam
|Affiliations:||Faculty of Information and Communication Technology (ICT)
Faculty of Information and Communication Technology (ICT)
|Keywords:||Feature Selection;Large-scale machine learning;Big data analytics;Bioinformatics;Biomarker discovery||Issue Date:||2015||Publisher:||Springer International Publishing||Journal:||Advances in Information and Communication Technology||Volume:||456||Start page:||81||End page:||92||Conference:||5th IFIP International Conference on Computer Science and its Applications||Abstract:||
Large-scale feature selection is one of the most important fields in the big data domain that can solve real data problems, such as bioinformatics, where it is necessary to process huge amount of data. The efficiency of existing feature selection algorithms significantly downgrades, if not totally inapplicable, when data size exceeds hundreds of gigabytes, because most feature selection algorithms are designed for centralized computing architecture. For that, distributed computing techniques, such as MapReduce can be applied to handle very large data. Our approach is to scale the existing method for feature selection, Kmeans clustering and Signal to Noise Ratio (SNR) combined with optimization technique as Binary Particle Swarm Optimization (BPSO). The proposed method is divided into two stages. In the first stage, we have used parallel Kmeans on MapReduce for clustering features, and then we have applied iterative MapReduce that implement parallel SNR ranking for each cluster. After, we have selected the top ranked feature from each cluster. The top scored features from each cluster are gathered and a new feature subset is generated. In the second stage, the new feature subset is used as input to the proposed BPSO based on MapReduce which provides an optimized feature subset. The proposed method is implemented in a distributed environment, and its efficiency is illustrated through analyzing practical problems such as biomarker discovery.
|Appears in Collections:||Journal Articles|
Show full item record
Files in This Item:
|Kourid-2015-Biomarker-discovery-based-on-large-.pdf||220.53 kB||Adobe PDF||View/Open|
checked on Feb 25, 2023
checked on Feb 25, 2023
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
Built with DSpace-CRIS - Extension maintained and optimized by