100 citations · 104 across the 3 of their papers we have counts for
4 papers
MIHT: A Hoeffding Tree for Time Series Classification using Multiple Instance Learning
Aurora Esteban, Amelia Zafra, Sebastián Ventura
Due to the prevalence of temporal data and its inherent dependencies in many real-world problems, time series classification is of paramount importance in various domains. However,…
Hoeffding adaptive trees for multi-label classification on data streams
Aurora Esteban, Alberto Cano, Amelia Zafra +1
Data stream learning is a very relevant paradigm because of the increasing real-world scenarios generating data at high velocities and in unbounded sequences. Stream learning aims…
Helping university students to choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization
A. Esteban, A. Zafra, C. Romero
The wide availability of specific courses together with the flexibility of academic plans in university studies reveal the importance of Recommendation Systems (RSs) in this area.…
MIML library: a Modular and Flexible Library for Multi-instance Multi-label Learning
Álvaro Belmonte, Amelia Zafra, Eva Gibaja
MIML library is a Java software tool to develop, test, and compare classification algorithms for multi-instance multi-label (MIML) learning. The library includes 43 algorithms and…