Výsledky vyhledávání - "Afef Ben Brahim"

Stable feature selection based on instance learning, redundancy elimination and efficient subsets fusion

Autor: Afef Ben Brahim

Publikováno v: Neural Computing and Applications. 33:1221-1232

Feature selection is frequently used as a preprocessing step to data mining and is attracting growing attention due to the increasing amounts of data emerging from different domains. The large data dimensionality increases the noise and thus the erro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d0a0736f60554c579748717e4a0bba3f
https://doi.org/10.1007/s00521-020-04971-y

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Improving Customer Relationship Management using Machine Learning techniques: A Tunisian Case Study

Autor: Wafa Raboudi, Afef Ben Brahim

Publikováno v: OCTA

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d280196b74698cbbc2e5f2152cf089fd
https://doi.org/10.1109/octa49274.2020.9151465

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Ensemble feature selection for high dimensional data: a new method and a comparative study

Autor: Mohamed Limam, Afef Ben Brahim

Publikováno v: Advances in Data Analysis and Classification. 12:937-952

The curse of dimensionality is based on the fact that high dimensional data is often difficult to work with. A large number of features can increase the noise of the data and thus the error of a learning algorithm. Feature selection is a solution for

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::15e1bf45a488fc21513669908d42a5cb
https://doi.org/10.1007/s11634-017-0285-y

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A hybrid feature selection method based on instance learning and cooperative subset search

Autor: Afef Ben Brahim, Mohamed Limam

Publikováno v: Pattern Recognition Letters. 69:28-34

A hybrid feature selection method is proposed for classification in small sample size data sets.The filter step is based on instance learning taking advantage of the small sample size of data.A few candidate feature subsets are generated since their

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d60b91b1436ae539d96c4524f1204ff1
https://doi.org/10.1016/j.patrec.2015.10.005

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Semi Supervised Relevance Learning for Feature Selection on High Dimensional Data

Autor: Alexandros Kalousis, Afef Ben Brahim

Publikováno v: AICCSA

Nowadays, the advanced technologies make amounts of data growing in a fast paced way. In many application fields, this trend concerns specially dimensions of the data. It is the case where features are about thousands and tens of thousands, while the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a5e497c8ba77c0fbc70168306892ef3d
https://doi.org/10.1109/aiccsa.2017.192

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A Hybrid Embedded-Filter Method for Improving Feature Selection Stability of Random Forests

Autor: Wassila Jerbi, Afef Ben Brahim, Nadia Essoussi

Publikováno v: Advances in Intelligent Systems and Computing ISBN: 9783319529400
HIS

Many domains deal with high dimensional data that are described with few observations compared to the large number of features. Feature selection is frequently used as a pre-processing step to make mining such data more efficient. Actually, the issue

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::364e44ca5542e7caf8eaa01d4fb6ada6
https://doi.org/10.1007/978-3-319-52941-7_37

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Sequential Instance Based Feature Subset Selection for High Dimensional Data

Autor: Afef Ben Brahim, Mohamed Limam

Publikováno v: Mining Intelligence and Knowledge Exploration ISBN: 9783319268316
MIKE

Feature subset selection is a key problem in the data-mining classification task that helps to obtain more compact and understandable models without degrading their performance. This paper deals with the problem of supervised wrapper based feature su

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::4ecdf7b0ecefa98cd02720ca083b9644
https://doi.org/10.1007/978-3-319-26832-3_5

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New prior knowledge based extensions for stable feature selection

Autor: Afef Ben Brahim, Mohamed Limam

Publikováno v: SoCPaR

In many data sets, there are only hundreds or fewer samples but thousands of features. The relatively small number of samples in high dimensional data results in modest classification performance and feature selection instability. In order to deal wi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::36d6c00b3f27a67e390c2bec9f5d81fc
https://doi.org/10.1109/socpar.2014.7008024

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A Stable Instance Based Filter for Feature Selection in Small Sample Size Data Sets

Autor: Afef Ben Brahim, Mohamed Limam

Publikováno v: Advanced Data Mining and Applications ISBN: 9783319147161
ADMA

In supervised feature selection applications it is common to have high dimensional data, but it is sometimes not easy to collect a large number of examples to represent each pattern or object class. Hence, learning in the small sample case is of prac

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2155252deeedf48c3da30853554f87f8
https://doi.org/10.1007/978-3-319-14717-8_26

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