53 citations · 106 across the 8 of their papers we have counts for
8 papers
Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spaces
Iñigo López-Riobóo Botana, Carlos Eiras-Franco, Julio Hernandez-Castro +1
Most proposals in the anomaly detection field focus exclusively on the detection stage, specially in the recent deep learning approaches. While providing highly accurate prediction…
Explain and Conquer: Personalised Text-based Reviews to Achieve Transparency
Iñigo López-Riobóo Botana, Verónica Bolón-Canedo, Bertha Guijarro-Berdiñas +1
There are many contexts in which dyadic data are present. Social networks are a well-known example. In these contexts, pairs of elements are linked building a network that reflects…
E2E-FS: An End-to-End Feature Selection Method for Neural Networks
Brais Cancela, Verónica Bolón-Canedo, Amparo Alonso-Betanzos
Classic embedded feature selection algorithms are often divided in two large groups: tree-based algorithms and lasso variants. Both approaches are focused in different aspects: whi…
Community detection and Social Network analysis based on the Italian wars of the 15th century
J. Fumanal-Idocin, A. Alonso-Betanzos, O. Cordón +2
In this contribution we study social network modelling by using human interaction as a basis. To do so, we propose a new set of functions, affinities, designed to capture the natur…
On the effectiveness of convolutional autoencoders on image-based personalized recommender systems
E. Blanco-Mallo, B. Remeseiro, V. Bolón-Canedo +1
Recommender systems (RS) are increasingly present in our daily lives, especially since the advent of Big Data, which allows for storing all kinds of information about users' prefer…
A scalable saliency-based Feature selection method with instance level information
Brais Cancela, Verónica Bolón-Canedo, Amparo Alonso-Betanzos +1
Classic feature selection techniques remove those features that are either irrelevant or redundant, achieving a subset of relevant features that help to provide a better knowledge…
Distributed Correlation-Based Feature Selection in Spark
Raul-Jose Palma-Mendoza, Luis de-Marcos, Daniel Rodriguez +1
CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains. We describe Distributed CFS (DiCFS) as a…
An Information Theoretic Feature Selection Framework for Big Data under Apache Spark
Sergio Ramírez-Gallego, Héctor Mouriño-Talín, David Martínez-Rego +4
With the advent of extremely high dimensional datasets, dimensionality reduction techniques are becoming mandatory. Among many techniques, feature selection has been growing in int…