A multiobjective based automatic framework for classifying cancer-microRNA biomarkers

Saha, Sriparna; Mitra, Sayantan; Yadav, Ravi Kant

doi:10.1016/j.genrep.2016.04.001

Cited by 6 publications

(8 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We call these approaches pre-model because the optimization happens prior to model generation. Examples are: tuning a neural network's learning rate; a SVM's type of kernel function (Rosales-Pérez et al, 2017); L2 regularization ; and random forests' number of trees (Saha et al, 2016).…”

Section: Model Optimizationmentioning

confidence: 99%

“…Pre-model approaches may support heterogeneous sets of base learners. For example, in Saha et al (2016) the authors select both the types of base learner and their respective hyper-parameters, together with a set of attributes that will be assigned to a given learner. They used the NSGA-II (Deb et al, 2002) algorithm, and the one-point crossover keeps base models and hyper-parameters together, only allowing to swap the selected attributes for each model.…”

Section: Model Optimizationmentioning

confidence: 99%

“…Several application domains have benefited from EA-based ensemble learning algorithms, including for example wind speed prediction Zhang et al, 2017a), cancer detection (Krawczyk et al, 2013;Krawczyk et al, 2015Krawczyk et al, , 2016Singh et al, 2016), noise bypass detection in vehicles (Redel-Macías et al, 2013), stock market prediction (Chen et al, 2007;Mabu et al, 2014Mabu et al, , 2015, and microarray data classification (Park & Cho, 2003bLiu et al, 2007Liu et al, , 2014aLiu et al, , 2015Rapakoulia et al, 2014;Saha et al, 2016), to name just a few.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A survey of evolutionary algorithms for supervised ensemble learning

Cagnini

Dôres

Freitas

et al. 2023

The Knowledge Engineering Review

View full text Add to dashboard Cite

This paper presents a comprehensive review of evolutionary algorithms that learn an ensemble of predictive models for supervised machine learning (classification and regression). We propose a detailed four-level taxonomy of studies in this area. The first level of the taxonomy categorizes studies based on which stage of the ensemble learning process is addressed by the evolutionary algorithm: the generation of base models, model selection, or the integration of outputs. The next three levels of the taxonomy further categorize studies based on methods used to address each stage. In addition, we categorize studies according to the main types of objectives optimized by the evolutionary algorithm, the type of base learner used and the type of evolutionary algorithm used. We also discuss controversial topics, like the pros and cons of the selection stage of ensemble learning, and the need for using a diversity measure for the ensemble’s members in the fitness function. Finally, as conclusions, we summarize our findings about patterns in the frequency of use of different methods and suggest several new research directions for evolutionary ensemble learning.

show abstract

Section: Model Optimizationmentioning

confidence: 99%

Section: Model Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A survey of evolutionary algorithms for supervised ensemble learning

Cagnini

Dôres

Freitas

et al. 2023

The Knowledge Engineering Review

View full text Add to dashboard Cite

show abstract

“…In [25], the classification of miRNAs was proposed to differentiate between normal and tumor tissues by using a multi-objective evolutionary-optimization technique. In that optimization strategy, the automatic selection of the classifier, its parameters, and feature combination steps were performed.…”

Section: Review Of Computational Intelligence Techniquesmentioning

confidence: 99%

A Review of Computational Methods for Finding Non-Coding RNA Genes

Abbas

Raza

Biyabani

et al. 2016

Genes

View full text Add to dashboard Cite

Finding non-coding RNA (ncRNA) genes has emerged over the past few years as a cutting-edge trend in bioinformatics. There are numerous computational intelligence (CI) challenges in the annotation and interpretation of ncRNAs because it requires a domain-related expert knowledge in CI techniques. Moreover, there are many classes predicted yet not experimentally verified by researchers. Recently, researchers have applied many CI methods to predict the classes of ncRNAs. However, the diverse CI approaches lack a definitive classification framework to take advantage of past studies. A few review papers have attempted to summarize CI approaches, but focused on the particular methodological viewpoints. Accordingly, in this article, we summarize in greater detail than previously available, the CI techniques for finding ncRNAs genes. We differentiate from the existing bodies of research and discuss concisely the technical merits of various techniques. Lastly, we review the limitations of ncRNA gene-finding CI methods with a point-of-view towards the development of new computational tools.

show abstract

“…Using ncRNA sequences as input and extracting sequence features, machine learning methods can complete the training of ncRNA recognition model and automatically classify ncRNA sequences. In [12], a multi-objective evolutionary method is proposed to classify miRNA for differencing tumor tissues from normal tissues. In [13] a convolution neural network method is proposed to predict miRNA.…”

Section: Introductionmentioning

confidence: 99%

A Fast and Robust Support Vector Machine With Anti-Noise Convex Hull and its Application in Large-Scale ncRNA Data Classification

2019

IEEE Access

View full text Add to dashboard Cite

Support vector machine (SVM) achieves successful classification performance with the application in non-coding RNA (ncRNA) data. With the rapid increase of the species and sizes of ncRNA sequences, several fast SVM methods based on data distribution and contour information have been developed to reduce their time complexity. However, they are sensitive to both noise and class imbalance problems. In this paper, a fast and robust SVM with anti-noise convex hull for large-scale ncRNA data classification (called FRSVM-ANCH) is proposed. FRSVM-ANCH discards the outliers in the feature space and obtains the convex hull of different classes. Then, the convex hull as the training data, along with its weight is used to train the SVM. Due to less sensitive to noise, pinball loss is adopted in SVM classifier. Theoretical analysis and experimental results verify the advantages of FRSVM-ANCH in classification performance and training time on large scale noisy and imbalanced ncRNA datasets.INDEX TERMS Large scale ncRNA, support vector machine, convex hull, anti-noise, class imbalance.

show abstract

A multiobjective based automatic framework for classifying cancer-microRNA biomarkers

Cited by 6 publications

References 27 publications

A survey of evolutionary algorithms for supervised ensemble learning

A survey of evolutionary algorithms for supervised ensemble learning

A Review of Computational Methods for Finding Non-Coding RNA Genes

A Fast and Robust Support Vector Machine With Anti-Noise Convex Hull and its Application in Large-Scale ncRNA Data Classification

Contact Info

Product

Resources

About