Ya-Han Hu

Artificial Intelligence · 2012–2019

A researcher in the Science corpus: 3 papers; trusts 6 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it; #21,783 of 35,345 by trust here, #19,439 by citations
• Accounting — 34 citations into it; #5,131 of 8,970 by trust here, #6,386 by citations
• Electrical and Electronic Engineering — 16 citations into it
• Management Science and Operations Research — 11 citations into it; #6,756 of 11,946 by trust here, #6,563 by citations
• Evaluation & Benchmarks — 10 citations into it
• Optimization & Training — 6 citations into it

Whom Ya-Han Hu trusts overall

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

1. Leo Breiman
2. Lyn C. Thomas
3. Teuvo Kohonen
4. John R. Koza
5. P. Ravi Kumar
6. Vadlamani Ravi

Who trusts Ya-Han Hu overall

1. Ningshan Chen
2. Victor-Emil Neagoe
3. Kee-Hoon Kim
4. Jamie Hayes

Papers from Ya-Han Hu's perspective

Ordered by how strongly the researchers Ya-Han Hu trusts endorse each paper. Same corpus, a different order for every researcher.

1. Statistical Learning Theory
2. Statistical Classification Methods in Consumer Credit Scoring: a Review
3. A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers
4. Genetic Programming: On the Programming of Computers by Means of Natural Selection
5. On Combining Classifiers
6. FORECASTING WITH ARTIFICIAL NEURAL NETWORKS: THE STATE OF THE ART
7. Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review
8. Neural networks in business: techniques and applications for the operations researcher (Computers & Operations Research)
9. 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems
10. How effective are neural networks at forecasting and prediction? A review and evaluation

Their papers

• Machine Learning in Financial Crisis Prediction: A Survey (2012) — 312 citations
• Under-sampling class imbalanced datasets by combining clustering analysis and instance selection (2019) — 295 citations
• Clustering-based undersampling in class-imbalanced data (2017) — 783 citations