ROSEFW-RF: The winner algorithm for the ECBDL’14 big data competition: An extremely imbalanced big data bioinformatics problem

Triguero, Isaac; Río, Sara del; López, Victoria; Bacardit, Jaume; Benítez, José Manuel; Herrera, Francisco

doi:10.1016/j.knosys.2015.05.027

Cited by 128 publications

(70 citation statements)

References 36 publications

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“…Modern systems generate massive amounts of information that forces us to develop computationally effective solutions for processing them. Big data can also be affected by class imbalance, posing increased challenge to learning systems [55]. Not only the increasing data volume can become prohibitive for existing methods, but also the nature of problem can cause additional difficulties.…”

Section: Imbalanced Big Datamentioning

confidence: 99%

Learning from imbalanced data: open challenges and future directions

2016

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Despite more than two decades of continuous development learning from imbalanced data is still a focus of intense research. Starting as a problem of skewed distributions of binary tasks, this topic evolved way beyond this conception. With the expansion of machine learning and data mining, combined with the arrival of big data era, we have gained a deeper insight into the nature of imbalanced learning, while at the same time facing new emerging challenges. Data-level and algorithm-level methods are constantly being improved and hybrid approaches gain increasing popularity. Recent trends focus on analyzing not only the disproportion between classes, but also other difficulties embedded in the nature of data. New real-life problems motivate researchers to focus on computationally efficient, adaptive and real-time methods. This paper aims at discussing open issues and challenges that need to be addressed to further develop the field of imbalanced learning. Seven vital areas of research in this topic are identified, covering the full spectrum of learning from imbalanced data: classification, regression, clustering, data streams, big data analytics and applications, e.g., in social media and computer vision. This paper provides a discussion and suggestions concerning lines of future research for each of them.

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Section: Imbalanced Big Datamentioning

confidence: 99%

Learning from imbalanced data: open challenges and future directions

2016

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Section: Preliminary Results and Discussionmentioning

confidence: 99%

“…It comes from the Evolutionary Big Data Competition ECBDL'14 [24], [25]. For this study, we consider a subset of 10% of the instances, in which the number of features was reduced from 631 to 90 by means of the feature selection algorithm applied in [25]. This dataset contains a total of 3,489,083 instances, from which 69,133 In our experiments we consider a 5-fold stratified crossvalidation model, meaning that we construct 5 random partitions of each dataset maintaining the prior probabilities of each class.…”

Section: Preliminary Results and Discussionmentioning

confidence: 99%

A first attempt on global evolutionary undersampling for imbalanced big data

Triguero

Galar

Bustince

et al. 2017

2017 IEEE Congress on Evolutionary Computation (CEC)

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Abstract-The design of efficient big data learning models has become a common need in a great number of applications. The massive amounts of available data may hinder the use of traditional data mining techniques, especially when evolutionary algorithms are involved as a key step. Existing solutions typically follow a divide-and-conquer approach in which the data is split into several chunks that are addressed individually. Next, the partial knowledge acquired from every slice of data is aggregated in multiple ways to solve the entire problem. However, these approaches are missing a global view of the data as a whole, which may result in less accurate models.In this work we carry out a first attempt on the design of a global evolutionary undersampling model for imbalanced classification problems. These are characterised by having a highly skewed distribution of classes in which evolutionary models are being used to balance it by selecting only the most relevant data. Using Apache Spark as big data technology, we have introduced a number of variations to the well-known CHC algorithm to work very large chromosomes and reduce the costs associated to fitness evaluation. We discuss some preliminary results, showing the great potential of this new kind of evolutionary big data model.

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“…The ROS pre-processing method for big data made SVM effective for pairwise ortholog detection and improved the performance of Random Forest for big data even more with a higher value for the resampling size parameter of 130% [41]. Conversely, the experiments showed that the variation in this parameter value from 100 to 130% did not significantly influence on the performance of the SVM big data classifier with different regulation values.…”

Section: Discussionmentioning

confidence: 99%

Big Data Supervised Pairwise Ortholog Detection in Yeasts

Cañizares¹,

Barrio‐García²,

Herrera³

et al. 2017

Yeast - Industrial Applications

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Ortholog are genes in different species, evolving from a common ancestor. Ortholog detection is essential to study phylogenies and to predict the function of unknown genes. The scalability of gene (or protein) pairwise comparisons and that of the classification process constitutes a challenge due to the ever-increasing amount of sequenced genomes. Ortholog detection algorithms, just based on sequence similarity, tend to fail in classification, specifically, in Saccharomycete yeasts with rampant paralogies and gene losses. In this book chapter, a new classification approach has been proposed based on the combination of pairwise similarity measures in a decision system that consider the extreme imbalance between ortholog and non-ortholog pairs. Some new gene pair similarity measures are defined based on protein physicochemical profiles, gene pair membership to conserved regions in related genomes, and protein lengths. The efficiency and scalability of the calculation of these measures are analyzed to propose its implementation for big data. In conclusion, evaluated supervised algorithms that manage big and imbalanced data showed high effectiveness in Saccharomycete yeast genomes.

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ROSEFW-RF: The winner algorithm for the ECBDL’14 big data competition: An extremely imbalanced big data bioinformatics problem

Cited by 128 publications

References 36 publications

Learning from imbalanced data: open challenges and future directions

Learning from imbalanced data: open challenges and future directions

A first attempt on global evolutionary undersampling for imbalanced big data

Big Data Supervised Pairwise Ortholog Detection in Yeasts

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