Conference talks

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Euroscipy 2018

Imbalanced-learn: A scikit-learn-contrib to tackle learning from imbalanced data set

Abstract: The curse of imbalanced data set refers to data sets in which the number of samples in one class is less than in others. This issue is often encountered in real world data sets such as medical imaging applications (e.g. cancer detection), fraud detection, etc. In such particular condition, machine learning algorithms learn sub-optimal models which will generally favor the class having the largest number of samples. In this talk, we review the different available strategy to learn a statistical model under those specific condition. Then, we will present imbalanced-learn package and the new features which will be released in the new version 0.4.

Slides Package

CDS Pitching Day 2017

RAMP on predicting autism from resting-state functional MRI and anatomical MRI

Abstract: This talk will present the ongoing preparation of a RAMP aiming at distinguishing subjects with Autism Spectrum Disorder (ASD) from typical control subjects. This analysis will use the Autism Brain Imaging Data Exchange (ABIDE I & II) database and data from Robert Debre Hospital based on R-fMRI and anatomical MRI. We will particularly focus on presenting the problematic, the typical pipeline answering this problem, and the current status of this RAMP. This work is in collaboration with the Pasteur Institute (Neuroanatomy group of the Unit of Human Genetics and Cognitive Functions).

Slides

Euroscipy 2017

Leverage knowledge from under-represented classes in machine learning: imbalanced-learn release 0.3.0

Abstract: The curse of imbalanced data set refers to data sets in which the number of samples in one class is less than in others. This issue is often encountered in real world data sets such as medical imaging applications (e.g. cancer detection), fraud detection, etc. In such particular condition, machine learning algorithms learn sub-optimal models which will generally favor the class having the largest number of samples. In this talks, we present the new feature which are available in the release 0.3.0.

Slides Package

PyParis 2017

Leverage knowledge from under-represented classes in machine learning: an introduction to imbalanced-learn

Abstract: The curse of imbalanced data set refers to data sets in which the number of samples in one class is less than in others. This issue is often encountered in real world data sets such as medical imaging applications (e.g. cancer detection), fraud detection, etc. In such particular condition, machine learning algorithms learn sub-optimal models which will generally favor the class having the largest number of samples. In this talk, we will present the imbalanced-learn package which implement some of the state-of-the-art algorithms, tackling the class imbalance problem.

Slides Package