| 
        
          	|  |  
          	|  |   |  |   
		|  | 
 
 | 
 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 |  |  | 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 |  |  | 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 |  |  | 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 |  |  | 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 |  |  | 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 |  |  | 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 |  | 
 |  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
 
 
 |  |   
	  	|  | ORBEL  - Conference chairs: Diego Cattaruzza and Maxime Ogier 
 
 |  |  
          |  |  |