Chih-Fong Tsai

Artificial Intelligence · 2008–2019

A researcher in the Science corpus: 15 papers; trusts 66 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 145 citations into it; #6,501 of 35,345 by trust here, #5,880 by citations
• Accounting — 53 citations into it; #1,726 of 8,970 by trust here, #3,887 by citations
• Information Systems — 34 citations into it; #6,587 of 8,771 by trust here, #6,770 by citations
• Evaluation & Benchmarks — 30 citations into it
• Statistics and Probability — 27 citations into it
• Management Science and Operations Research — 26 citations into it; #1,534 of 11,946 by trust here, #2,402 by citations
• Computer Networks and Communications — 22 citations into it; #7,404 of 9,886 by trust here, #1,992 by citations
• Civil and Structural Engineering — 21 citations into it; #2,570 of 6,160 by trust here, #2,398 by citations

Whom Chih-Fong Tsai trusts overall

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

1. Teuvo Kohonen
2. Ron Kohavi
3. Amir F. Atiya
4. Kyoung-jae Kim
5. John R. Koza
6. Bernhard Schölkopf
7. I-Cheng Yeh
8. Alex Smola
9. Anil K. Jain
10. Leo Breiman

Who trusts Chih-Fong Tsai overall

1. Jie Sun
2. Arash Bahrammirzaee
3. Yuan Zhang
4. Ningshan Chen
5. Ajay Byanjankar
6. Martin Längkvist
7. Chaouki Khammassi
8. Antanas Verikas
9. Hao Chen
10. Marcel Karnstedt

Papers from Chih-Fong Tsai's perspective

Ordered by how strongly the researchers Chih-Fong Tsai trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Going deeper with convolutions
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Using neural network ensembles for bankruptcy prediction and credit scoring (2008) — 526 citations
• Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches (2010) — 382 citations
• Machine Learning in Financial Crisis Prediction: A Survey (2012) — 312 citations
• Missing value imputation: a review and analysis of the literature (2006–2017) (2019) — 549 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
• SVM and SVM Ensembles in Breast Cancer Prediction (2017) — 304 citations
• Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study (2016) — 366 citations
• The distance function effect on k-nearest neighbor classification for medical datasets (2016) — 429 citations
• CANN: An intrusion detection system based on combining cluster centers and nearest neighbors (2015) — 514 citations
• Machine learning in concrete strength simulations: Multi-nation data analytics (2014) — 439 citations
• Genetic algorithms in feature and instance selection (2013) — 237 citations
• Concrete compressive strength analysis using a combined classification and regression technique (2012) — 150 citations
• Intrusion detection by machine learning: A review (2009) — 1,037 citations
• Customer churn prediction by hybrid neural networks (2009) — 316 citations