Feature Selection Based on Graph Representation

Akhiat, Yassine; Chahhou, Mohamed; Zinedine, Ahmed

doi:10.1109/cist.2018.8596467

Cited by 8 publications

(9 citation statements)

References 5 publications

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“…Feature selection is an active research filed in machine learning, as it is an important pre-processing, finding success in different real problem applications. In general, feature selection algorithms are categorized into supervised, Semi-supervised and Unsupervised feature selection [2,3,4,5,6]. Supervised feature selection methods usually come in three flavors: Filter, Wrapper and Embedded approach.…”

Section: Feature Selection Methods Classificationmentioning

confidence: 99%

“…There are two key stages in the filtering process. To cause the classification model, it first ranks features individually based on a particular criterion measure such as distance, Pearson correlation, and entropy [4]. Second, it selects the best-ranked features using a threshold value.…”

Section: Filter Methodsmentioning

confidence: 99%

“…1). As a result, learning models built with the chosen subset of features are more readable and interpretable [3,4,5,25].The main reasons behind using feature selection are: Reducing the storage capacity and execution time, preventing the curse of dimensionality problem, minimizing the over-fitting issue, resulting in improved model generalization and increasing the performance attainability [6].…”

Section: Introductionmentioning

confidence: 99%

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Feature Selection: A Review and Comparative Study

Bouchlaghem

Akhiat²,

Amjad

2022

E3S Web Conf.

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Feature selection (FS) is an important research topic in the area of data mining and machine learning. FS aims at dealing with the high dimensionality problem. It is the process of selecting the relevant features and removing the irrelevant, redundant and noisy ones, intending to obtain the best performing subset of original features without any transformation. This paper provides a comprehensive review of FS literature intending to supplement insights and recommendations to help readers. Moreover, an empirical study of six well-known feature selection methods is presented so as to critically analyzing their applicability.

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Section: Feature Selection Methods Classificationmentioning

confidence: 99%

Section: Filter Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feature Selection: A Review and Comparative Study

Bouchlaghem

Akhiat²,

Amjad

2022

E3S Web Conf.

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“…Due to the massive increase in data amount in real-world datasets, Feature Selection (FS) becomes a necessary pre-processing technique to reduce dimensionality. FS is the process of choosing relevant features and removing redundant, irrelevant and noisy ones [1][2][3]25]. Generally, Feature selection aims to:…”

Section: Introductionmentioning

confidence: 99%

A New Noisy Random Forest Based Method for Feature Selection

Akhiat¹,

Manzali²,

Chahhou

et al. 2021

Cybernetics and Information Technologies

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Feature selection is an essential pre-processing step in data mining. It aims at identifying the highly predictive feature subset out of a large set of candidate features. Several approaches for feature selection have been proposed in the literature. Random Forests (RF) are among the most used machine learning algorithms not just for their excellent prediction accuracy but also for their ability to select informative variables with their associated variable importance measures. Sometimes RF model over-fits on noisy features, which lead to choosing the noisy features as the informative variables and eliminating the significant ones. Whereas, eliminating and preventing those noisy features first, the low ranked features may become more important. In this study we propose a new variant of RF that provides unbiased variable selection where a noisy feature trick is used to address this problem. First, we add a noisy feature to a dataset. Second, the noisy feature is used as a stopping criterion. If the noisy feature is selected as the best splitting feature, then we stop the creation process because at this level, the model starts to over-fit on the noisy features. Finally, the best subset of features is selected out of the best-ranked feature regarding the Geni impurity of this new variant of RF. To test the validity and the effectiveness of the proposed method, we compare it with RF variable importance measure using eleven benchmarking datasets.

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“…Em aprendizado máquina, grafos são usados para modelar e suportar tarefas de aprendizagem profunda (Rossi;Ahmed, 2020;BACCIU et al, 2020). Em seleção e analise de caraterísticas, representações por grafos permitem a exploração de padrões entre as caraterísticas (Akhiat;Chahhou;Zinedine, 2018;MINGHIM et al, 2020). Em redes complexas grafos que modelam dados complexos também são utilizados para tarefas de descrição de caraterísticas (SCABINI et al, 2017;Hajij;Munch;Rosen, 2020).…”

Section: Lista De Tabelasunclassified

Propagação de etiquetas para segmentação de imagens

Belizario¹

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Image segmentation is an important task in image processing, usually employed in more complex computer vision tasks. In graph clustering-based segmentation approaches, the image is modeled by a graph, in which vertices are generally represented by pixels and edges by weights that denote similarity between pixels. The problems associated with graph-based approaches usually concern the computational cost and the high cardinality of the graphs, which translates into the large number of vertices and edges necessary to generate an adequate representation of the image. Among segmentation approaches with graphs, those based on detection of communities in complex networks, such as Label Propagation, in particular because they have a lower computational cost, stand out. However, such methods when applied directly to images, do not generate accurate results, in addition to being non-deterministic, which is an undesirable quality in image segmentation. On the other hand, superpixels techniques, which combine several pixels, are important not only in reducing the cardinality of the graphs, but also in providing greater descriptive power compared to a single pixel. This doctoral thesis presents a new family of segmentation methods for images of large natural scenes images based on the Label Propagation and superpixels method, with deterministic behavior and which uses specific information from the image domain. Algorithms were developed for both automatic segmentation (SGLP-Simple Graph Label Propagation and MGLP-Multi-level Label Propagation), and for interactive segmentation (IGLP-Interactive Graph Label Propagation), which demand user assistance. Quantitative results show a PRI precision of 0.83 and error percentage of Er 6.13%, for the automatic and interactive version, respectively. Results were also obtained in the processing time of 0.0048 s and 0.29 s, for automatic and interactive segmentation. These results were corroborated in several experiments on standard data sets. When compared with related methods, the results of the methods are superior both in mean precision and time for automatic segmentation. As for the interactive segmentation method (IGLP), segmentation mean precision was slightly outperformed by state of the art methods, but run in shorter times.

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Feature Selection Based on Graph Representation

Cited by 8 publications

References 5 publications

Feature Selection: A Review and Comparative Study

Feature Selection: A Review and Comparative Study

A New Noisy Random Forest Based Method for Feature Selection

Propagação de etiquetas para segmentação de imagens

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