Feature clustering and ranking for selecting stable features from high dimensional remotely sensed data

Harris, Dugal; Niekerk, Adriaan van

doi:10.1080/01431161.2018.1500730

Cited by 9 publications

(7 citation statements)

References 28 publications

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“…The feature selection is either performed with the filter model [ 18 , 47 ] or with the wrapper model [ 22 ]. Harris and Niekerk proposed a feature clustering and ranking (FCR) approach where feature clustering is performed using the affinity propagation algorithm associated with a correlation coefficient as a similarity measure [ 21 ]. A single feature from each of the top ranked clusters is then selected by using either a filter model or a wrapper one according to two different evaluation measures.…”

Section: Related Workmentioning

confidence: 99%

“…When dealing with high-dimensional data, many feature selection approaches can successfully remove irrelevant features but fail to pull redundant ones out [ 18 , 19 ]. To overcome this problem, several feature selection algorithms that use feature clustering were proposed in recent decades in both supervised and unsupervised contexts [ 18 , 20 , 21 , 22 , 23 , 24 ]. This paper focuses on clustering-based feature selection approaches in a supervised context.…”

Section: Introductionmentioning

confidence: 99%

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Compact Hybrid Multi-Color Space Descriptor Using Clustering-Based Feature Selection for Texture Classification

et al. 2022

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Color texture classification aims to recognize patterns by the analysis of their colors and their textures. This process requires using descriptors to represent and discriminate the different texture classes. In most traditional approaches, these descriptors are used with a predefined setting of their parameters and computed from images coded in a chosen color space. The prior choice of a color space, a descriptor and its setting suited to a given application is a crucial but difficult problem that strongly impacts the classification results. To overcome this problem, this paper proposes a color texture representation that simultaneously takes into account the properties of several settings from different descriptors computed from images coded in multiple color spaces. Since the number of color texture features generated from this representation is high, a dimensionality reduction scheme by clustering-based sequential feature selection is applied to provide a compact hybrid multi-color space (CHMCS) descriptor. The experimental results carried out on five benchmark color texture databases with five color spaces and manifold settings of two texture descriptors show that combining different configurations always improves the accuracy compared to a predetermined configuration. On average, the CHMCS representation achieves 94.16% accuracy and outperforms deep learning networks and handcrafted color texture descriptors by over 5%, especially when the dataset is small.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Compact Hybrid Multi-Color Space Descriptor Using Clustering-Based Feature Selection for Texture Classification

et al. 2022

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“…It is shown that SVM-RNE has a good performance and also improves the biological interpretability of the results. Studies similar to SVM-RCE and SVM-RNE were later carried out by different groups [ 31 , 32 ], which indicates the importance and the merit of the SVM-RCE approach. The study of Ref.…”

Section: Gene Selection Approaches For Gene Expression Datasetsmentioning

confidence: 99%

Application of Biological Domain Knowledge Based Feature Selection on Gene Expression Data

Yousef

Kumar

Bakır-Güngör

2020

Entropy

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In the last two decades, there have been massive advancements in high throughput technologies, which resulted in the exponential growth of public repositories of gene expression datasets for various phenotypes. It is possible to unravel biomarkers by comparing the gene expression levels under different conditions, such as disease vs. control, treated vs. not treated, drug A vs. drug B, etc. This problem refers to a well-studied problem in the machine learning domain, i.e., the feature selection problem. In biological data analysis, most of the computational feature selection methodologies were taken from other fields, without considering the nature of the biological data. Thus, integrative approaches that utilize the biological knowledge while performing feature selection are necessary for this kind of data. The main idea behind the integrative gene selection process is to generate a ranked list of genes considering both the statistical metrics that are applied to the gene expression data, and the biological background information which is provided as external datasets. One of the main goals of this review is to explore the existing methods that integrate different types of information in order to improve the identification of the biomolecular signatures of diseases and the discovery of new potential targets for treatment. These integrative approaches are expected to aid the prediction, diagnosis, and treatment of diseases, as well as to enlighten us on disease state dynamics, mechanisms of their onset and progression. The integration of various types of biological information will necessitate the development of novel techniques for integration and data analysis. Another aim of this review is to boost the bioinformatics community to develop new approaches for searching and determining significant groups/clusters of features based on one or more biological grouping functions.

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“…It is shown that SVM-RNE has good performance and also improves the biological interpretability of the results. Studies similar to SVM-RCE and SVM-RNE were later carried out by different groups [31], [32] , which indicates the importance and the merit of the SVM-RCE approach. The study of [33] has a slightly modified SVM-RCE algorithm on the disease state prediction step.…”

Section: Mitra Et Al Adopted Clustering Large Applications Based Upomentioning

confidence: 99%

Application of Biological Domain Knowledge Based Feature Selection on Gene Expression Data

Yousef¹,

Kumar²,

Bakır-Güngör³

2020

Preprint

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In the last two decades, there have been massive advancements in high throughput technologies, which resulted in the exponential growth of public repositories of gene expression datasets for various phenotypes. It is possible to unravel biomarkers by comparing the gene expression levels under different conditions, such as disease vs. control, treated vs. not treated, drug A vs. drug B, etc. This problem refers to a well-studied problem in the machine learning domain, i.e., the feature selection problem. In biological data analysis, most of the computational feature selection methodologies were taken from other fields, without considering the nature of the biological data. For gene expression data analysis, most of the existing feature selection methods rely on expression values alone to select the genes; and biological knowledge is integrated at the end of the analysis in order to gain biological insights or to support the initial findings. Thus, integrative approaches that utilize the biological knowledge while performing feature selection are necessary for this kind of data. The main idea behind the integrative gene selection process is to generate a ranked list of genes considering both the statistical metrics that are applied to the gene expression data, and the biological background information which is provided as external datasets. Since the integrative approach attracted attention in the gene expression domain, lately the gene selection process shifted from being purely data-centric to more incorporative analysis with additional biological knowledge.

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Feature clustering and ranking for selecting stable features from high dimensional remotely sensed data

Cited by 9 publications

References 28 publications

Compact Hybrid Multi-Color Space Descriptor Using Clustering-Based Feature Selection for Texture Classification

Compact Hybrid Multi-Color Space Descriptor Using Clustering-Based Feature Selection for Texture Classification

Application of Biological Domain Knowledge Based Feature Selection on Gene Expression Data

Application of Biological Domain Knowledge Based Feature Selection on Gene Expression Data

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