4 citations · 7 across the 8 of their papers we have counts for
5 papers · 1 filter
ck-means, a novel unsupervised learning method that combines fuzzy and crispy clustering methods to extract intersecting data
Jean-Sébastien Dessureault, Daniel Massicotte
Clustering data is a popular feature in the field of unsupervised machine learning. Most algorithms aim to find the best method to extract consistent clusters of data, but very few…
Explainable Global Error Weighted on Feature Importance: The xGEWFI metric to evaluate the error of data imputation and data augmentation
Jean-Sébastien Dessureault, Daniel Massicotte
Evaluating the performance of an algorithm is crucial. Evaluating the performance of data imputation and data augmentation can be similar since both generated data can be compared…
DPDR: A novel machine learning method for the Decision Process for Dimensionality Reduction
Jean-Sébastien Dessureault, Daniel Massicotte
This paper discusses the critical decision process of extracting or selecting the features in a supervised learning context. It is often confusing to find a suitable method to redu…
Feature selection or extraction decision process for clustering using PCA and FRSD
Jean-Sebastien Dessureault, Daniel Massicotte
This paper concerns the critical decision process of extracting or selecting the features before applying a clustering algorithm. It is not obvious to evaluate the importance of th…
Unsupervised Machine learning methods for city vitality index
Jean-Sébastien Dessureault, Jonathan Simard, Daniel Massicotte
This paper concerns the challenge to evaluate and predict a district vitality index (VI) over the years. There is no standard method to do it, and it is even more complicated to do…