Category: Research

Over the last few years, the increasing interest in machine learning has resulted in the design and development of several competitive learners. Usually, the performance of these methods is evaluated by comparing the new techniques to state-of-the-art methods over a collection of real-world problems.

In early days, these comparisons followed no standard, and qualitative arguments where used to extract conclusions from the results. Although these types of analyses enabled highlighting key points about the results, they also depended, to a certain extent, on the eyes of the beholder. Therefore, the need for finding a saver framework to analyze the results arose. With these, several researchers started drawing a methodology based on statistical tests. In the last three years, the first papers appeared on that topic. One of the first contributions can be found in the paper “Statistical Comparisons of Classifiers over Multiple Data Sets” by Janez Demsar. Later on, several authors extended this first efforts to build a save environment for results analysis.

And even more recently, Francisco Herrera and his research group gathered all these efforts and made a tutorial which is available here. The tutorial explains how different tests work and draws different ways to take when applying a statistical analysis to your results.

Find below the presentation of the paper Beyond Homemade Artificial Data Sets by Núria Macià, Albert Orriols-Puig, and Ester Bernadó-Mansilla in the 2009 Hybrid Artificial Intelligence Systems (HAIS’09).

This work aims at creating boundedly difficult problems for data classification whose complexity moves through different dimensions. For this purpose, this work proposes the use of a multi-objective optimization procedure to create data sets that satisfy different criteria of complexity. Please, refer to a preprint of the paper for more information.

Tomorrow, the international Hybrid Artificial Intelligence Systems conference (HAIS) gets started in Salamanca with the special session of Knowledge Extraction based on Evolutionary Learning (KEEL). In this special session, the following 14 papers that use evolutionary algorithms for different purposes in the field of machine learning will be presented:

1. A hybrid bumble bees mating optimization – GRASP algorithm for clustering by Yannis Marinakis, Magdalene Marinaki, and Nikolaos Matsatsinis
2. A first study on the use of cooperative coevolution for instance and feature selection in classification with nearest neighbour rule by Joaquín Derrac, Salvador García, and Francisco Herrera
3. Unsupervised feature selection in high dimensional spaces and uncertainty by José R. Villar, María R. Suárez, Javier Sedano, and Felipe Mateos
4. Non-dominated multi-objective evolutionary algorithm based on fuzzy rules extraction for subgroup discovery by C. J. Carmona, P. González, M.J. del Jesus, and F. Herrera
5. A first study on the use of interval-valued fuzzy sets with genetic tuning for classification with imbalanced data-sets by J. Sanz, A. Fernández, H. Bustince, and F. Herrera
6. Feature construction and feature selection in presence of attribute interactions by Leila S. Shafti and Eduardo Pérez
7. Multiobjective evolutionary clustering approach to security vulnerability assessments by Guiomar Corral, Àlvaro Garcia-Piquer, Albert Orriols-Puig, Albert Fornells, and Elisabet Golobardes
8. Beyond homemade artificial data sets by Nuria Macià, Albert Orriols-Puig, and Ester Bernadó-Mansilla
9. A three-objective evolutionary approach to generate Mamdani fuzzy rule-based systems by Michela Antonelli, Pietro Ducange, Beatrice Lazzerini, and Francesco Marcelloni
10. A new component selection algorithm based on metrics and fuzzy clustering analysis by Camelia Serban, Andreea Vescan, and Horia F. Pop
11. Multilabel classification with gene expression programming by J. L. Ávila, E. L. Gibaja, and S. Ventura
12. An evolutionary ensemble-based method for rule extraction with distributed data by Diego M. Escalante, Miguel Angel Rodriguez, and Antonio Peregrin
13. Evolutionary extraction of association rules: A preliminary study on their effectiveness by Nicolò Flugy Papè, Jesús Alcalá-Fdez, Andrea Bonarini, and Francisco Herrera
14. A minimum-risk genetic fuzzy classifier based on low quality data by Ana M. Palacios, Luciano Sánchez, and Inés Couso

We’ll have to wait until tomorrow to know more what these promising titles hide.

A few days ago, Prof. Cirac was interviewed in a Catalan TV channel about his work on quantum theory of information. Prof. Cirac explained the method based on quantum cryptography that he and his team have been developing during the last few years, which makes sure that the information can be neither intercepted nor decrypted. Actually the information is not physically transmitted, but just appears at the receiver side.

In addition to the method itself, I was surprised by the clarity with which Prof. Cirac introduced quantum physics and reviewed some of its paradoxes. In what follows, you can find a link to the video. Unfortunately, the interview is only in Catalan (interviewer) and Spanish (Prof. Cirac).

Over the last few decades, it has been shown that GAs (and derivate methods such as GPs) are able to solve complex real-world problems and rediscover engineering and scientific findings which were originally deduced after many years of investigation. Recently, Hod Lipson and Michael Schmidt have provided the scientific community with another cool application of GAs. In this case, Lipson and Smith designed a system that was able to extrapolate the laws of motion from pendulum’s swings.

The program starts with a set of data that describes the pendulum’s swings. Then, the program first creates random combinations of basic mathematical processes such as addition, substraction, multiplication, division, and a few more algebraic operators. Therefore, each individual forms an equation that explains the data. Then, the population of individuals is evolved by the typical genetic operators. This approach resulted in equations that are very similar to the law of conservation of momentum and Newton’s law of motion.

The paper associated to this research has been recently published in Science.