Will N. Browne

Artificial Intelligence · 2013–2016

A researcher in the Science corpus: 3 papers; trusts 41 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it; #11,706 of 35,345 by trust here, #13,962 by citations
• Computational Theory and Mathematics — 17 citations into it; #4,263 of 12,182 by trust here, #4,365 by citations
• Computer Vision and Pattern Recognition — 13 citations into it
• Information Systems — 10 citations into it
• Molecular Biology — 9 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Statistical Learning Theory — 6 citations into it

Whom Will N. Browne trusts overall

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

1. Durga Prasad Muni
2. Manoranjan Dash
3. Huan Liu
4. Li-Fei Chen
5. Bing Xue
6. Klaus Nordhausen
7. Ron Kohavi
8. Li-Yeh Chuang
9. Isabelle M Guyon
10. Shih-Wei Lin

Who trusts Will N. Browne overall

1. Bing Xue
2. Shenkai Gu
3. Mengjie Zhang
4. Yu Xue
5. Ran Cheng

Papers from Will N. Browne's perspective

Ordered by how strongly the researchers Will N. Browne trusts endorse each paper. Same corpus, a different order for every researcher.

1. Wrappers for Feature Subset Selection
2. An Introduction to Variable and Feature Selection
3. Feature Selection for Classification
4. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
5. Toward integrating feature selection algorithms for classification and clustering (IEEE Transactions on Knowledge and Data Engineering)
6. Efficient Feature Selection via Analysis of Relevance and Redundancy (Journal of machine learning research)
7. Selection bias in gene extraction on the basis of microarray gene-expression data (Proceedings of the National Academy of Sciences of the United States of America)
8. A Simplex Method for Function Minimization
9. Feature selection based on rough sets and particle swarm optimization (Pattern Recognition Letters)
10. Particle swarm optimization

Their papers

• A Survey on Evolutionary Computation Approaches to Feature Selection (2016) — 1,551 citations
• Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach (2013) — 557 citations
• Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms (2014) — 566 citations