Network


Latest external collaboration on country level. Dive into details by clicking on the dots.

Hotspot


Dive into the research topics where Alain Berro is active.

Publication


Featured researches published by Alain Berro.


Annals of Mathematics and Artificial Intelligence | 2010

Genetic algorithms and particle swarm optimization for exploratory projection pursuit

Alain Berro; Souad Larabi Marie-Sainte; Anne Ruiz-Gazen

Exploratory Projection Pursuit (EPP) methods have been developed thirty years ago in the context of exploratory analysis of large data sets. These methods consist in looking for low-dimensional projections that reveal some interesting structure existing in the data set but not visible in high dimension. Each projection is associated with a real valued index which optima correspond to valuable projections. Several EPP indices have been proposed in the statistics literature but the main problem lies in their optimization. In the present paper, we propose to apply Genetic Algorithms (GA) and recent Particle Swarm Optimization (PSO) algorithm to the optimization of several projection pursuit indices. We explain how the EPP methods can be implemented in order to become an efficient and powerful tool for the statistician. We illustrate our proposal on several simulated and real data sets.


Electronic Notes in Discrete Mathematics | 2010

Time dependent multiobjective best path for multimodal urban routing

Tristram Gräbener; Alain Berro; Yves Duthen

While the fastest path problem has been widely studied with excellent results, little research has been done on the time dependent multiobjective best paths. Applied to multimodal urban routing, this approach offers multiple suggestions adapted to variety of user preferences. We propose a simple model with intersting properties that allows to use traditional algorithms with little modifications. The experimental computation time are acceptable for a real world application.


Archive | 2010

Detecting Multivariate Outliers Using Projection Pursuit with Particle Swarm Optimization

Anne Ruiz-Gazen; Souad Larabi Marie-Sainte; Alain Berro

Detecting outliers in the context of multivariate data is known as an important but difficult task and there already exist several detection methods. Most of the proposed methods are based either on the Mahalanobis distance of the observations to the center of the distribution or on a projection pursuit (PP) approach. In the present paper we focus on the one-dimensional PP approach which may be of particular interest when the data are not elliptically symmetric. We give a survey of the statistical literature on PP for multivariate outliers etection and investigate the pros and cons of the different methods. We also propose the use of a recent heuristic optimization algorithm called Tribes for multivariate outliers detection in the projection pursuit context.


international conference on swarm intelligence | 2010

An efficient optimization method for revealing local optima of projection pursuit indices

Souad Larabi Marie-Sainte; Alain Berro; Anne Ruiz-Gazen

In order to summarize and represent graphically multidimensional data in statistics, projection pursuit methods look for projection axes which reveal structures, such as possible groups or outliers, by optimizing a function called projection index. To determine these possible interesting structures, it is necessary to choose an optimization method capable to find not only the global optimum of the projection index but also the local optima susceptible to reveal these structures. For this purpose, we suggest a metaheuristic which does not ask for many parameters to settle and which provokes premature convergence to local optima. This method called Tribes is a hybrid Particle Swarm Optimization method (PSO) based on a stochastic optimization technique developed in [2]. The computation is fast even for big volumes of data so that the use of the method in the field of projection pursuit fulfills the statistician expectations.


international conference on intelligent transportation systems | 2007

Improvement of a Shortest Routes Algorithm

Nicolas Lassabe; Alain Berro; Yves Duthen

This article describes an original shortest path algorithm for graphs representing a real road network. We test the influence of a coefficient weighing the evaluated distance used in such short path algorithms. This coefficient has an influence on the performance of the shortest path algorithm which uses the evaluated distance as heuristic. We try to provide a better algorithm using this heuristic and we test its evaluation in various situations. The results presented in this paper are used to write new shortest path algorithms for large real road networks. To conclude, we experiment this algorithm and some alternatives in various situations.


advances in databases and information systems | 2015

A Content-Driven ETL Processes for Open Data

Alain Berro; Imen Megdiche; Olivier Teste

The emergent statistical Open Data (OD) seems very promising to generate various analysis scenarios for decision-making systems. Nevertheless, OD has problematic characteristics such as semantic and structural heterogeneousness, lack of schemas, autonomy and dispersion. These characteristics shakes the traditional Extract-Transform-Load (ETL) processes since these latter generally deal with well structured schemas. We propose in this paper a content-driven ETL processes which automates ”as far as possible” the extraction phase based only on the content of flat Open Data sources. Our processes rely on data annotations and data mining techniques to discover hierarchical relationships. Processed data are then transformed into instance-schema graphs to facilitate the structural data integration and the definition of the multidimensional schemas of the data warehouse.


international conference on enterprise information systems | 2015

Graph-based ETL Processes for Warehousing Statistical Open Data

Alain Berro; Imen Megdiche; Olivier Teste

Warehousing is a promising mean to cross and analyse Statistical Open Data (SOD). But extracting structures, integrating and defining multidimensional schema from several scattered and heterogeneous tables in the SOD are major problems challenging the traditional ETL (Extract-Transform-Load) processes. In this paper, we present a three step ETL processes which rely on RDF graphs to meet all these problems. In the first step, we automatically extract tables structures and values using a table anatomy ontology. This phase converts structurally heterogeneous tables into a unified RDF graph representation. The second step performs a holistic integration of several semantically heterogeneous RDF graphs. The optimal integration is performed through an Integer Linear Program (ILP). In the third step, system interacts with users to incrementally transform the integrated RDF graph into a multidimensional schema.


Lecture Notes in Computer Science | 2005

Distributed anticipatory system

Marco A. Ramos; Alain Berro; Yves Duthen

In this paper we present an introduction to computing anticipatory systems. The internals aspects of anticipation will be explained. The concepts of incursion are proposed to model anticipatory systems. A simple example of computing anticipatory systems will be simulated on computer that includes an anticipatory model.


research challenges in information science | 2015

Holistic Statistical Open Data integration based on integer linear programming

Alain Berro; Imen Megdiche; Olivier Teste

Integrating several Statistical Open Data (SOD) tables is a very promising issue. Various analysis scenarios are hidden behind these statistical data, which makes it important to have a holistic view of them. However, as these data are scattered in several tables, it is a slow and costly process to use existing pairwise schema matching approaches to integrate several schemas of the tables. Hence, we need automatic tools that rapidly converge to a holistic integrated view of data and give a good matching quality. In order to accomplish this objective, we propose a new 0-1 linear program, which automatically resolves the problem of holistic OD integration. It performs global optimal solutions maximizing the profit of similarities between OD graphs. The program encompasses different constraints related to graph structures and matching setup, in particular 1:1 matching. It is solved using a standard solver (CPLEX) and experiments show that it can handle several input graphs and good matching quality compared to existing tools.


genetic and evolutionary computation conference | 2004

Autonomous Agent for Multi-objective Optimization

Alain Berro; Stéphane Sanchez

In this article, we present an agent-based method associated with a local research inspired by strategies of evolution to solve multiobjective problems. In comparison with GA-based methods this method uses few parameters. Moreover a decision maker can easily understand the influence of these parameters on the result. The conception of this method led us to represent the Pareto optimal set with zones and not with points. This representation gives additional information which allows to choose between two non-dominated solutions.

Collaboration


Dive into the Alain Berro's collaboration.

Top Co-Authors

Avatar
Top Co-Authors

Avatar
Top Co-Authors

Avatar
Top Co-Authors

Avatar

Yves Duthen

University of Toulouse

View shared research outputs
Top Co-Authors

Avatar
Top Co-Authors

Avatar
Top Co-Authors

Avatar
Top Co-Authors

Avatar
Top Co-Authors

Avatar
Top Co-Authors

Avatar
Researchain Logo
Decentralizing Knowledge