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--- teaching:mfe:ia [2017/04/20 18:33]
stuetzle
+++ teaching:mfe:ia [2018/04/19 17:07]
stuetzle [Text Categorisation and quality control through automatic language processing]
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-====== MFE 2015-2016 : Intelligence Artificielle ======
+====== MFE 2017-2018 : Intelligence Artificielle ======
 ===== Introduction =====
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   * Contact : [[http://code.ulb.ac.be/iridia.people.php?id=1|Hugues Bersini (IRIDIA)]]
+===== Text Categorisation and quality control through automatic language processing =====
+This thesis is developed in collaboration with the Energy Efficiency in Industrial Processes (EEIP) company. EEIP is a global industry information network. As part of their activities, they disseminate case studies to various network groups. The goal of the project is to develop an automatic language processing algorithm capable to evaluate the quality (accept / reject) of the proposed case studies and to allocate them to single/multiple categories. Testing and training the algorithm is a key part as it not only requires development and testing of concepts such as how to evaluate quality or definition of requirements for multiple category allocation but the project also has
+to be developed in a limited data environment (+/- 1000 case studies as training set).
+Required skills: A background in machine learning would be helpful.
+  * Contacts :
+    * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]]
+    * [[https://www.ee-ip.org/|Jürgen Ritzek (EE-IP)]]
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-===== Software framework for ant colony optimization =====
+===== Automated configuration of multi-objective continuous optimizers =====
-Ants have inspired a number of computational techniques and among the most successful is ant colony optimization (ACO). ACO is an optimization technique that can be applied to tackle a wide variety of computational problems that arise in computer science, telecommunications, and engineering. While ACO has a very wide applicability, the development times for effective ACO algorithms can be relatively high. This is due to the fact that each time a new problem is to be tackled by an ACO algorithm, a researcher needs to implement the algorithms almost from scratch.
+Many problems arising in real-world applications involve the optimization of various, often conflicting objectives. While the design of algorithms for tackling multi-objective problems has usually done manually, over the recent years automated design methodologies have been established and proved to be very powerful.
-The goal of the project is to provide a software framework to support the application and the implementation of ACO algorithms to new problems. The software framework will offer all the standard procedures that are used in ACO algorithms and will allow for the rapid prototyping of ACO algorithms.
+The goal of this project is to extend the automated design to multi-objective continuous optimization problems. As the basis of the approach, a framework based on the two-phase plus Pareto local search approach will be developed into which basic search techniques for continuous optimization will be integrated. The goal is to build first a flexible framework from which then in a second step effective multi-objective optimizers will be generated exploiting automated algorithm design techniques. The final goal of this work is to participate in algorithm competitions with the goal of challenging the methodology.
-The application of this software framework will be tested on a number of optimization problems.
-Required skills: The candidate should be well acquainted with programming in object oriented languages.
+Required skills: The candidate should have very good analytical as well as programming skills.
   * Contacts :
-    * [[http://iridia.ulb.ac.be/~mdorigo|Marco Dorigo (IRIDIA)]]
     * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]]