Comparative study of techniques for large-scale feature selection
The combinatorial search problem arising in feature selection in high dimensional spaces is
considered. Recently developed techniques based on the classical sequential methods and …
considered. Recently developed techniques based on the classical sequential methods and …
Boosted whale optimization algorithm with natural selection operators for software fault prediction
Software fault prediction (SFP) is a challenging process that any successful software should
go through it to make sure that all software components are free of faults. In general, soft …
go through it to make sure that all software components are free of faults. In general, soft …
Feature subset selection by Bayesian network-based optimization
A new method for Feature Subset Selection in machine learning, FSS-EBNA (Feature
Subset Selection by Estimation of Bayesian Network Algorithm), is presented. FSS-EBNA is …
Subset Selection by Estimation of Bayesian Network Algorithm), is presented. FSS-EBNA is …
Hybrid binary dragonfly algorithm with simulated annealing for feature selection
There are various fields are affected by the growth of data dimensionality. The major
problems which are resulted from high dimensionality of data including high memory …
problems which are resulted from high dimensionality of data including high memory …
Solving feature subset selection problem by a parallel scatter search
The aim of this paper is to develop a Parallel Scatter Search metaheuristic for solving the
Feature Subset Selection Problem in classification. Given a set of instances characterized by …
Feature Subset Selection Problem in classification. Given a set of instances characterized by …
Musical instrument recognition by pairwise classification strategies
Musical instrument recognition is an important aspect of music information retrieval. In this
paper, statistical pattern recognition techniques are utilized to tackle the problem in the …
paper, statistical pattern recognition techniques are utilized to tackle the problem in the …
BHHO-TVS: A binary harris hawks optimizer with time-varying scheme for solving data classification problems
Data classification is a challenging problem. Data classification is very sensitive to the noise
and high dimensionality of the data. Being able to reduce the model complexity can help to …
and high dimensionality of the data. Being able to reduce the model complexity can help to …
[PDF][PDF] A novel face recognition approach based on genetic algorithm optimization
M Moussa, M Hmila, A Douik - Studies in Informatics and Control, 2018 - sic.ici.ro
In the field of image processing and recognition, discrete cosine transform (DCT) and
principal component analysis (PCA) are two widely used techniques. In this paper we …
principal component analysis (PCA) are two widely used techniques. In this paper we …
An intelligent hybrid feature subset selection and production pattern recognition method for modeling ethylene plant
Q Li, M Zhang, X Shi, X Lan, X Guo, Y Guan - Journal of Analytical and …, 2021 - Elsevier
A data-driven model framework integrating Feature Subset Selection (FSS), production
pattern clustering analysis and prediction was proposed for predicting ethylene yield of …
pattern clustering analysis and prediction was proposed for predicting ethylene yield of …
A hybrid of genetic algorithm and support vector machine for features selection and classification of gene expression microarray
Constantly improving gene expression technology offer the ability to measure the
expression levels of thousand of genes in parallel. Gene expression data is expected to …
expression levels of thousand of genes in parallel. Gene expression data is expected to …