Amparo Alonso-Betanzos

Artificial Intelligence · 2007–2019

A researcher in the Science corpus: 8 papers; trusts 44 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 87 citations into it; #10,998 of 35,345 by trust here, #12,083 by citations
• Molecular Biology — 60 citations into it; #6,243 of 17,773 by trust here, #7,911 by citations
• Computer Vision and Pattern Recognition — 42 citations into it; #9,969 of 21,575 by trust here, #9,187 by citations
• Information Systems — 20 citations into it; #2,988 of 8,771 by trust here, #5,501 by citations
• Evaluation & Benchmarks — 18 citations into it
• Statistics and Probability — 11 citations into it
• ML Systems & Hardware — 9 citations into it
• Computer Networks and Communications — 8 citations into it

Whom Amparo Alonso-Betanzos trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Huan Liu
2. Lei Yu
3. Mark A. Hall
4. Igor Kononenko
5. Verónica Bolón-Canedo
6. Zheng Zhao
7. George Forman
8. Manoranjan Dash
9. Jerome H. Friedman
10. Noelia Sánchez-Maroño

Who trusts Amparo Alonso-Betanzos overall

1. Verónica Bolón-Canedo
2. Noelia Sánchez-Maroño
3. Nicholas Pudjihartono
4. Prachi Agrawal
5. Aisha Abdallah
6. Vipin Kumar
7. Bilal Mirza

Papers from Amparo Alonso-Betanzos's perspective

Ordered by how strongly the researchers Amparo Alonso-Betanzos trusts endorse each paper. Same corpus, a different order for every researcher.

1. An Introduction to Variable and Feature Selection
2. ImageNet classification with deep convolutional neural networks
3. Wrappers for Feature Subset Selection
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. Estimating Attributes: Analysis and Extensions of RELIEF
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. The WEKA data mining software: an update

Their papers

• A review of feature selection methods on synthetic data (2013) — 836 citations
• A review of microarray datasets and applied feature selection methods (2014) — 638 citations
• Recent advances and emerging challenges of feature selection in the context of big data (2015) — 328 citations
• Centralized vs. distributed feature selection methods based on data complexity measures (2017) — 66 citations
• Ensembles for feature selection: A review and future trends (2019) — 468 citations
• Feature selection for high-dimensional data (2016) — 388 citations
• Multispectral classification of grass weeds and wheat (Triticum durum) using linear and nonparametric functional discriminant analysis and neural networks (2008) — 58 citations
• Filter Methods for Feature Selection - A Comparative Study (2007) — 427 citations