Genetic algorithm-based heuristic for feature selection in credit risk assessment

By Stjepan Oreski, Goran Oreski. Expert systems with applications, 2014

408 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/f7f72289cad21dd156955a0d31e4bdf5b69c3ec4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

1. Xin Yao
2. Bing Xue
3. Mengjie Zhang
4. Will N. Browne

In Artificial Intelligence

1. Bing Xue
2. Mengjie Zhang
3. Will N. Browne
4. Yaochu Jin
5. Xin Yao
6. Gilles Trédan
7. Erwan Le Merrer
8. Ran Cheng

In Computational Theory and Mathematics

1. Mengjie Zhang
2. Bing Xue
3. Will N. Browne
4. Xin Yao
5. Yaochu Jin
6. Ran Cheng
7. Ponnuthurai Nagaratnam Suganthan
8. Carlos Artemio Coello Coello

Citations in the corpus

• The Bouncer Problem: Challenges to Remote Explainability (2019, background): “90% for a hybrid between genetic algorithm and a neural network [22].”
• A Survey on Evolutionary Computation Approaches to Feature Selection (2016, methodology): “A potential limitation in [67], [72], and [85] is that the removed lowlyranked features might become highly useful when combined with other features because of feature interaction.”