Arbre de décision

vignette| Arbre de décision Un arbre de décision est un outil d'aide à la décision représentant un ensemble de choix sous la forme graphique d'un arbre. Les différentes décisions possibles sont situées aux extrémités des branches (les « feuilles » de l'arbre), et sont atteintes en fonction de décisions prises à chaque étape. L'arbre de décision est un outil utilisé dans des domaines variés tels que la sécurité, la fouille de données, la médecine, etc. Il a l'avantage d'être lisible et rapide à exécuter. Il s'agit de plus d'une représentation calculable automatiquement par des algorithmes d'apprentissage supervisé. Présentation Les arbres de décision sont utilisés dans des domaines d'aide à la décision (par exemple l'informatique décisionnelle) ou l'exploration de données. Ils décrivent comment répartir une population d'individus (clients d'une entreprise, utilisateurs d'un réseau social, …) en groupes homogènes selon un ensemble de variables discriminantes (âge, temps pass

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Forecasting price spikes in European day-ahead electricity markets using decision trees

Anna Fragkioudaki

Although the techniques for normal price prediction are well advanced, price spike prediction remains a challenge. The aim of this paper is to present a method to predict both normal day-ahead electricity prices as well as day-ahead price spikes using classification and regression trees. The models are build using historical data of European electricity market and transmission system variables. The proposed method is tested on several bidding zones of Europe. © 2015 IEEE.

IEEE2015

Chargement

Presentation and study of robustness for several methods to classify individuals based on their gene expressions

Julien Damond

Several studies have shown that it is possible to detect cancer tissues based on gene expressions using methods of machine learning. The main problem with classifying gene expression data is to obtain accurate rules that are easy to interpret and provide indications for follow up studies. Indeed high accuracy is hard to achieve due to the small number of observations and the large amount of genes in the human genome. Some methods of machine learning are based on an important quantity of genes, which lead to decision rules that are usually difficult to interpret. These methods were tested on different samples and their results were compared. Most of them provided good results with a high accuracy. Among these methods for gene classification one distanced itself from the others by producing transparents results which were readily interpretable and were very useful for follow up studies. It highlighted pair of genes that were the most efficient to classify individuals with respect to their gene expressions. This is the so called Top Scoring Pair (TSP) classifier. This method achieves prediction rates that are as high as those of the other methods. In contrast to other classifiers which use considerably more genes and more complicated procedures, the TSP has an easy and quick implementation and involves very few genes, namely only two. This provides very easy rules that are accurate and transparent. Finally, the TSP is paramter-free, which avoids overfitting and inflation of the estimation of the prediction rate.

2011

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Integration of scientific and social networks

Ehsaneddin Asgari

In this paper, we address the problem of scientific-social network integration to find a matching relationship between members of these networks (i.e. The DBLP publication network and the Twitter social network). This task is a crucial step toward building a multi environment expert finding system that has recently attracted much attention in Information Retrieval community. In this paper, the problem of social and scientific network integration is divided into two sub problems. The first problem concerns finding those profiles in one network, which presumably have a corresponding profile in the other network and the second problem concerns the name disambiguation to find true matching profiles among some candidate profiles for matching. Utilizing several name similarity patterns and contextual properties of these networks, we design a focused crawler to find high probable matching pairs, then the problem of name disambiguation is reduced to predict the label of each candidate pair as either true or false matching. Because the labels of these candidate pairs are not independent, state-of-the-art classification methods such as logistic regression and decision tree, which classify each instance separately, are unsuitable for this task. By defining matching dependency graph, we propose a joint label prediction model to determine the label of all candidate pairs simultaneously. Two main types of dependencies among candidate pairs are considered for designing the joint label prediction model which are quite intuitive and general. Using the discriminative approaches, we utilize various feature sets to train our proposed classifiers. An extensive set of experiments have been conducted on six test collection collected from the DBLP and the Twitter networks to show the effectiveness of the proposed joint label prediction model.

Springer2014

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EE-311: Fundamentals of machine learning

Ce cours présente une vue générale des techniques d'apprentissage automatique, passant en revue les algorithmes, le formalisme théorique et les protocoles expérimentaux.

CS-401: Applied data analysis

This course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the data science world (pandas, scikit-learn, Spark, etc.)

COM-406: Foundations of Data Science

We discuss a set of topics that are important for the understanding of modern data science but that are typically not taught in an introductory ML course. In particular we discuss fundamental ideas and techniques that come from probability, information theory as well as signal processing.

Théorie des jeux

La théorie des jeux est un domaine des mathématiques qui propose une description formelle d'interactions stratégiques entre agents (appelés « joueurs »). Les fondements mathématiques de la théorie m

Arbre de décision (apprentissage)

L’apprentissage par arbre de décision désigne une méthode basée sur l'utilisation d'un arbre de décision comme modèle prédictif. On l'utilise notamment en fouille de données et en apprentissage automa

Apprentissage automatique

L'apprentissage automatique (en anglais : machine learning, « apprentissage machine »), apprentissage artificiel ou apprentissage statistique est