Improved classification of large imbalanced data sets using rationalized technique: Updated Class Purity Maximization Over_Sampling Technique (UCPMOT)

The huge variety of NoSQL Big Data has tossed a need for new pathways to store, process and analyze it. The quantum of data created is inconceivable along with a mixed breath of unknown veracity and creative visualization. The new trials of frameworks help to find substantial unidentified values fro...

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Bibliographic Details
Published in:Journal of big data 2017-12, Vol.4 (1), p.1-32, Article 49
Main Authors: Patil, Sachin S., Sonavane, Shefali P.
Format: Article
Language:English
Subjects:
Big Data
Classification
Classifiers
Communications Engineering
Computational Science and Engineering
Computer Science
Data management
Data Mining and Knowledge Discovery
Database Management
Datasets
Distributed processing
Imbalanced data sets
Information Storage and Retrieval
Lowest versus highest
Mathematical Applications in Computer Science
Maximization
Multi-class
Networks
Over_Sampling Techniques
Performance enhancement
Purity
Sampling
Sampling techniques
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Summary:The huge variety of NoSQL Big Data has tossed a need for new pathways to store, process and analyze it. The quantum of data created is inconceivable along with a mixed breath of unknown veracity and creative visualization. The new trials of frameworks help to find substantial unidentified values from massive data sets. They have added an exceptional dimension to the pre-processing and contextual conversion of the data sets for needful analysis. In addition, handling of ambitious imbalanced data sets has acknowledged an intimation of alarm. Traditional classifiers are unable to discourse the precise need of grouping for such data sets. Over_sampling of the minority classes help to improve the performance. Updated Class Purity Maximization Over_Sampling Technique (UCPMOT) is a rationalized technique proposed to handle imbalanced data sets using exclusive safe-level based synthetic sample creation. It addresses the multi-class problem in alignment to a newly induced method namely lowest versus highest. The projected technique experiments with several data sets from the UCI repository. The underlying bed of mapreduce environment encompasses the distributed processing approach on Apache Hadoop framework. Several classifiers help to authorize the classification results using parameters like F-measure and AUC values. The experimental conclusions quote the dominance of UCPMOT over the benchmarking techniques.
ISSN:2196-1115
2196-1115
DOI:10.1186/s40537-017-0108-1