What are decision trees?

By Carl Kingsford, Steven L. Salzberg. Nature Biotechnology, 2008

443 citations recorded by the source corpus.

Decision trees have been applied to problems such as assigning protein function and predicting splice sites. How do these classifiers work, what types of problems can they solve and what are their advantages over alternatives?

Subfields: Artificial Intelligence, Information Systems, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/47f3606cb33a698246656a1054a0654e8c1ed330

How researchers see this paper

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

In Artificial Intelligence

1. Rohitash Chandra
2. Azal Ahmad Khan
3. Omkar Chaudhari

In Information Systems

1. Rohitash Chandra
2. Azal Ahmad Khan
3. Omkar Chaudhari

In Molecular Biology

1. Julio César Díaz Vera
2. Xin Lai
3. Yujie You
4. Le Zhang
5. Suran Liu
6. Ying Pan
7. Huiru Zheng
8. Senyi Deng

Citations in the corpus

• A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, background): “Hence, decision trees have made a huge impact in the area of medicine, and biotechnology [63, 64] since transparency of the decision-making process is important.”
• Artificial intelligence in cancer target identification and drug discovery (2022, background): “Recently, the alternating decision tree (ADTree) 162 has made the classi fi cation procedure intuitive and easy to understand by adding an intuitive graphical model, and the algorithm builds decision trees over a user-de fi ned number of iterations using con fi dence-rated boosting, so it returns both…”