Zobrazeno 1 - 10 of 83 pro vyhledávání: '"Boutheina Ben Yaghlane"'
1
WPEviRC: A Multi-rules-based Classifier for Evidential Databases Without Class Label Ambiguities
Autor: Nassim Bahri, Mohamed Anis Bach Tobji, Boutheina Ben Yaghlane
Publikováno v: International Journal on Artificial Intelligence Tools. 31
Rule-based classifiers use a collection of high-quality rules to classify new data instances. They can be categorized according to the adopted classification strategy: Classifiers based on a single rule, and classifiers based on multiple rules. Many
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f5bb3a10f9a228f8d7288276101700fa
https://doi.org/10.1142/s0218213022600028
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4
Mining closed high utility itemsets based on propositional satisfiability
Autor: Said Jabbour, Amel Hidouri, Badran Raddaoui, Boutheina Ben Yaghlane
Publikováno v: Data and Knowledge Engineering
Data and Knowledge Engineering, Elsevier, 2021, 136 (101927:1-101927:15), ⟨10.1016/j.datak.2021.101927⟩
International audience; A high utility itemset mining problem is the question of recognizing a set of items that have utility values greater than a given user utility threshold. This generalization of the classical problem of frequent itemset mining
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e23a29d5b1421675c71e6ad9d8881e08
https://hal.archives-ouvertes.fr/hal-03466365
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5
A declarative framework for mining Top-k high utility itemsets
Autor: Boutheina Ben Yaghlane, Badran Raddaoui, Mouna Chebbah, Said Jabbour, Amel Hidouri
Publikováno v: Big Data Analytics and Knowledge Discovery: 23rd International Conference, DaWaK 2021, Virtual Event, September 27–30, 2021, Proceedings
DaWaK 2021: 23rd international conference on Big Data Analytics and Knowledge Discovery
DaWaK 2021: 23rd international conference on Big Data Analytics and Knowledge Discovery, Sep 2021, Linz (online), Austria. pp.250-256, ⟨10.1007/978-3-030-86534-4_24⟩
Big Data Analytics and Knowledge Discovery ISBN: 9783030865337
DaWaK
International audience; The problem of mining high utility itemsets entails identifying a set of items that yield the highest utility values based on a given user utility threshold. In this paper, we utilize propositional satisfiability to model the
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::21ff5e4da02835d4ce03b8f14e365224
https://hal.archives-ouvertes.fr/hal-03466369
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6
Associative Classifier for Evidential Data
Autor: Mohamed Anis Bach Tobji, Nassim Bahri, Boutheina Ben Yaghlane
Publikováno v: ICTAI
Associative classifiers are a family of classification techniques that use association rules to classify a new instance. Many works were proposed in this context, with a focus on how to select and combine the relevant association rules for classifica
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8c0fadf49de179d0c76f409e984b138d
https://doi.org/10.1109/ictai50040.2020.00192
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7
A SAT-Based Approach for Mining High Utility Itemsets from Transaction Databases
Autor: Boutheina Ben Yaghlane, Said Jabbour, Amel Hidouri, Badran Raddaoui
Publikováno v: International Conference on Big Data Analytics and Knowledge Discovery
International Conference on Big Data Analytics and Knowledge Discovery, Sep 2020, Bratislava, Slovakia
Big Data Analytics and Knowledge Discovery ISBN: 9783030590642
DaWaK
Mining high utility itemsets is a keystone in several data analysis tasks. High Utility Itemset Mining generalizes the frequent itemset mining problem by considering item quantities and weights. A high utility itemset is a set of items that appears i
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0bde8d0bf07fc46306732c3f0f82f145
https://hal.archives-ouvertes.fr/hal-03092027
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8
Rule-Based Classification for Evidential Data
Autor: Mohamed Anis Bach Tobji, Nassim Bahri, Boutheina Ben Yaghlane
Publikováno v: Lecture Notes in Computer Science ISBN: 9783030584481
SUM
In this paper, we tackle the problem of multi-rules based classification for evidential data, i.e., data where imperfection is modeled through the Evidence theory. In this setting, a new algorithm called EviRC is introduced. This method uses differen
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::06ff916fb4567c1c5ec6285cc71fdb9a
https://doi.org/10.1007/978-3-030-58449-8_17
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9
Modeling evidential databases as possible worlds
Autor: Fatma Ezzahra Bousnina, Boutheina Ben Yaghlane, Mohamed Anis Bach Tobji, Mouna Chebbah
Publikováno v: International Journal of Intelligent Systems
International Journal of Intelligent Systems, Wiley, 2018, 33 (6), pp.1146--1164. ⟨10.1002/int.21959⟩
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::33ac77894d73e3c22388adbf140d912d
https://doi.org/10.1002/int.21959
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10
Belief Measure of Expertise for Experts Detection in Question Answering Communities: case study Stack Overflow
Autor: Arnaud Martin, Boutheina Ben Yaghlane, Dorra Attiaoui
Publikováno v: KES
Online Question Answering Communities (Q& A C) provide a valuable amount of information in several topics. The major challenge with Q& A C is the detection of the authoritative users. When manipulating real world data, we have to deal with imperfecti
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b3296c0e98e268dcb2ecf22f6fc40a27
https://doi.org/10.1016/j.procs.2017.08.099
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