An efficient approach for feature construction of high-dimensional microarray data by random projections

Dimensionality reduction of microarray data is a very challenging task due to high computational time and the large amount of memory required to train and test a model. Genetic programming (GP) is a stochastic approach to solving a problem. For high dimensional datasets, GP does not perform as well...

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Bibliographic Details
Published in:PloS one 2018-04, Vol.13 (4), p.e0196385-e0196385
Main Authors: Tariq, Hassan, Eldridge, Elf, Welch, Ian
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Bayesian analysis
Bioinformatics
Biology and Life Sciences
Computational efficiency
Computer and Information Sciences
Computer applications
Computer memory
Computer science
Computing time
Data mining
Datasets
Decision trees
DNA microarrays
Engineering schools
Gene expression
Genetic algorithms
Genetic aspects
Histology
Learning algorithms
Lung cancer
Machine learning
Medical research
Medicine and Health Sciences
Melanoma
Memory
Model testing
Nuclear reprogramming
Physical Sciences
Physiological aspects
Placenta
Population
Principal components analysis
Proteins
Reduction
Research and Analysis Methods
Smoking
Stochasticity
Support vector machines
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Summary:Dimensionality reduction of microarray data is a very challenging task due to high computational time and the large amount of memory required to train and test a model. Genetic programming (GP) is a stochastic approach to solving a problem. For high dimensional datasets, GP does not perform as well as other machine learning algorithms. To explore the inherent property of GP to generalize models from low dimensional data, we need to consider dimensionality reduction approaches. Random projections (RPs) have gained attention for reducing the dimensionality of data with reduced computational cost, compared to other dimensionality reduction approaches. We report that the features constructed from RPs perform extremely well when combined with a GP approach. We used eight datasets out of which seven have not been reported as being used in any machine learning research before. We have also compared our results by using the same full and constructed features for decision trees, random forest, naive Bayes, support vector machines and k-nearest neighbor methods.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0196385