The landscape contest at ICPR 2010

Authors:
Núria Macià;Tin Kam Ho;Albert Orriols-Puig;Ester Bernadó-Mansilla
Affiliations:
Grup de Recerca en Sistemes Intel-ligents, La Salle - Universitat Ramon Llull, Barcelona, Spain;Statistics and Learning Research Department, Bell Laboratories, Alcatel-Lucent, Murray Hill, NJ;Grup de Recerca en Sistemes Intel-ligents, La Salle - Universitat Ramon Llull, Barcelona, Spain;Grup de Recerca en Sistemes Intel-ligents, La Salle - Universitat Ramon Llull, Barcelona, Spain
Venue:
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Year:
2010

Citing 14
Cited 2

Instance-Based Learning Algorithms

Machine Learning
C4.5: programs for machine learning

C4.5: programs for machine learning
The nature of statistical learning theory

The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization

Advances in kernel methods
Approximate statistical tests for comparing supervised classification learning algorithms

Neural Computation
Complexity Measures of Supervised Classification Problems

IEEE Transactions on Pattern Analysis and Machine Intelligence
Random Forests

Machine Learning
The Design of Innovation: Lessons from and for Competent Genetic Algorithms

The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Generating Accurate Rule Sets Without Global Optimization

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Data Complexity in Pattern Recognition (Advanced Information and Knowledge Processing)

Data Complexity in Pattern Recognition (Advanced Information and Knowledge Processing)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)

Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
In search of targeted-complexity problems

Proceedings of the 12th annual conference on Genetic and evolutionary computation
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation

Learner excellence biased by data set selection: A case for data characterisation and artificial data sets

Pattern Recognition
Domains of competence of the semi-naive Bayesian network classifiers

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

The landscape contest provides a new and configurable framework to evaluate the robustness of supervised classification techniques and detect their limitations. By means of an evolutionary multiobjective optimization approach, artificial data sets are generated to cover reachable regions in different dimensions of data complexity space. Systematic comparison of a diverse set of classifiers highlights their merits as a function of data complexity. Detailed analysis of their comparative behavior in different regions of the space gives guidance to potential improvements of their performance. In this paper we describe the process of data generation and discuss performances of several well-known classifiers as well as the contestants' classifiers over the obtained data sets.