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Schedule
Thursday, 30 January |
08:30-09:10 | Welcome-registration-coffee | B14-B15-B16 |
09:10-09:30 | Opening session | Amphi Cuccaroni |
09:30-10:30 | Plenary session - Adam Letchford (Chair: Martine Labbé) | Amphi Cuccaroni |
10:30-11:20 | Coffee break | B14-B15-B16 |
11:20-12:40 | Parallel sessions |
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Multi-objective Optimization Chair: Sara Tari Room: E501 |
Health-care Chair: Véronique François Room: E502 |
Public Transportation Chair: Javier Duran Micco Room: E503 |
Global Optimization Chair: Olivier Rigal Room: E601 |
Analytics 1 Chair: Rafael Van Belle Room: E602 |
12:40-14:10 | Lunch/ORBEL Board meeting | B14-B15-B16/E501 |
14:10-15:10 | Parallel sessions |
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Air, Rail and Multimodal Transportation Chair: Paola Paregrini Room: E501 |
Game Theory Chair: Lotte Verdnock Room: E502 |
Transportation of People Chair: Yves Molenbruch Room: E503 |
Multi-level Optimization Chair: Concepcion Dominguez Room: E601 |
Analytics 2 Chair: Vedavyas Etikala Room: E602 |
15:10-15:50 | Coffee break | B14-B15-B16 |
15:50-16:40 | Parallel sessions |
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Sport Timetabling Chair: Dries Goossens Room: E501 |
Project Management and Scheduling Chair: Fan Yang Room: E502 |
Rich Routing and Graphs Chair: Alexandre Bontems Room: E503 |
Logistics Chair: Yuan Yuan Room: E601 |
Analytics 3 Chair: Jari Peeperkorn Room: E602 |
16:50-17:30 | ORBEL general assembly | Amphi Cuccaroni |
19:00-20:00 | Cocktail | Bar Zango |
20:30-23:00 | Dinner | La Terrasse des Ramparts |
Friday, 31 January |
09:30-10:30 | Plenary session - Miguel F. Anjos (Chair: Luce Brotcorne) | Amphi Cuccaroni |
10:30-11:20 | Coffee break | B14-B15-B16 |
11:20-12:40 | Parallel sessions |
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Automatic Configuration and Metaheuristics Analysis Chair: Kenneth Sorensen Room: E501 |
Real Life and Integrated Problems Chair: Jeroen Belien Room: E502 |
Warehouse Management Chair: Harol Mauricio Gamez Room: E601 |
Analytics 4 Chair: Noureddine Kouaissah Room: E602 |
12:40-14:10 | Lunch | B14-B15-B16 |
14:10-15:10 | Parallel sessions |
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Orbel Award Chair: Jeroen Belien Room: E501 |
Optimization Chair: Julien Dewez Room: E502 |
Trucking Optimization Chair: Hatice Çalik Room: E601 |
Analytics 5 Chair: Diego Olaya Room: E602 |
15:10-15:50 | Coffee break | B14-B15-B16 |
15:50-16:50 | Parallel sessions |
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Learning and Optimization Chair: Johan Van Kerckhoven Room: E501 |
Lot-sizing and Inventory Chair: Philippe Chevalier Room: E502 |
Rich Routing Chair: Cristian Aguayo Room: E601 |
17:00-17:15 | ORBEL award and closing session | Amphi Cuccaroni |
17:15-18:30 | Closing cocktail | B14-B15-B16 |
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Thursday 11:20 - 12:40 Multi-objective Optimization Room E501 - Chair: Sara Tari
Thursday 11:20 - 12:40 Health-care Room E502 - Chair: Véronique François
Thursday 11:20 - 12:40 Public Transportation Room E503 - Chair: Javier Duran Micco
Thursday 11:20 - 12:40 Global Optimization Room E601 - Chair: Olivier Rigal
Thursday 11:20 - 12:40 Analytics 1 Room E602 - Chair: Rafael Van Belle
Thursday 14:10 - 15:10 Air, Rail and Multimodal Transportation Room E501 - Chair: Paola Pellegrini
Thursday 14:10 - 15:10 Game Theory Room E502 - Chair: Lotte Verdnock
Thursday 14:10 - 15:10 Transportation of People Room E503 - Chair: Yves Molenbruch
Thursday 14:10 - 15:10 Multi-level Optimization Room E601 - Chair: Concepcion Dominguez
Thursday 14:10 - 15:10 Analytics 2 Room E602 - Chair: Vedavyas Etikala
Thursday 15:50 - 16:50 Sport Timetabling Room E501 - Chair: Dries Goossens
Thursday 15:50 - 16:50 Project Management and Scheduling Room E502 - Chair: Fan Yang
Thursday 15:50 - 16:50 Rich Routing and Graphs Room E503 - Chair: Alexandre Bontems
Thursday 15:50 - 16:50 Logistics Room E601 - Chair: Silia Mertens
Thursday 15:50 - 16:50 Analytics 3 Room E602 - Chair: Jari Peeperkorn
Friday 11:20 - 12:40 Automatic Configuration and Metaheuristics Analysis Room E501 - Chair: Kenneth Sorensen
Friday 11:20 - 12:40 Real Life and Integrated Problems Room E502 - Chair: Jeroen Belien
Friday 11:20 - 12:40 Warehouse Management Room E601 - Chair: Harol Mauricio Gamez
Friday 11:20 - 12:40 Analytics 4 Room E602 - Chair: Noureddine Kouaissah
- Classification for Imbalanced Data Using Feature Selection and Undersampling Methods
You-jin Park (National Taipei University of Technology) Abstract: In data mining and machine learning, the real-world data involve a large number of features, and frequently suffer from problem of class imbalance. However, in general, all of the features are not necessary since many of them can be redundant or even irrelevant, which may decrease the performance of an employed algorithm, e.g., a classification algorithm, and also traditional classifiers for a highly imbalanced dataset tend to bias in majority classes and, as a result, the minority class samples are usually misclassified as majority class. So, to overcome these problems, a proper feature selection approach that identifies informative feature subsets from large dimensional datasets, and an efficient sampling technique that removes some majority samples or creates new minority class samples can be used. The feature selection aims to reduce the dimensionality of the data and increase the performance of an algorithm by selecting only a small, but suitable subset of relevant features from the original large-scale dataset. So, various useful feature selection algorithms have been developed and applied to the problem of finding a suitable subset of features in multivariate datasets, often posed as single- or multi-objective optimization problems. And also, to cope with classification for imbalanced data, many machine learning approaches have been developed, most of which have been based on sampling techniques, cost sensitive learning, and ensemble methods. Particularly, some undersampling approaches have been utilized to eliminate the harms of skewed distribution by discarding the intrinsic samples in the majority class. Therefore, in this research, we propose an efficient, but simple cluster-based undersampling method for resolving class imbalance problem with a new feature selection method based on pairwise comparison, and then compare the performance of the classification model to those of others models with respect to various performance measures.
- Multi-task Neural Networks for Uplift Modeling
Sam Verboven (Vrije Universiteit Brussel) Co-authors: Jeroen Berrevoets, Wouter Verbeke
- Portfolio selection using semiparametric estimators and a copula PCA based approach
Noureddine Kouaissah (International University of Rabat, Rabat Business School )
Friday 14:10 - 15:10 Orbel Award Room E501 - Chair: Jeroen Belien
Friday 14:10 - 15:10 Optimization Room E502 - Chair: Julien Dewez
Friday 14:10 - 15:10 Trucking Optimization Room E601 - Chair: Hatiz Çalik
Friday 14:10 - 15:10 Analytics 5 Room E602 - Chair: Diego Olaya
Friday 15:50 - 16:50 Learning and Optimization Room E501 - Chair: Jorik Jooken
Friday 15:50 - 16:50 Lot-sizing and Inventory Room E502 - Chair: Philippe Chevalier
Friday 15:50 - 16:50 Rich Routing Room E601 - Chair: Cristian Aguayo
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ORBEL - Conference chairs: Diego Cattaruzza and Maxime Ogier
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