Recursive Feature Generation for Knowledge-based Learning

By Lior Friedman, Shaul Markovitch. 2018

10 citations recorded by the source corpus.

When humans perform inductive learning, they often enhance the process with background knowledge. With the increasing availability of well-formed collaborative knowledge bases, the performance of learning algorithms could be significantly enhanced if a way were found to exploit these knowledge bases. In this work, we present a novel algorithm for injecting external knowledge into induction algorithms using feature generation. Given a feature, the algorithm defines a new learning task over its set of values, and uses the knowledge base to solve the constructed learning task. The resulting classifier is then used as a new feature for the original problem. We have applied our algorithm to the domain of text classification using large semantic knowledge bases. We have shown that the generated features significantly improve the performance of existing learning algorithms.

Subfields: Artificial Intelligence

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. Marc-André Zöller
2. Marco F. Huber
3. Li Yang
4. Abdallah Shami

Citations in the corpus

• Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “Except very few publications (e.g., Friedman & Markovitch, 2015; Smith et al., 2017) current AutoML algorithms are designed as a black-box.”
• Survey on Automated Machine Learning (2019, background): “Approaches to enhance automatic feature generation with domain knowledge, e.g., (Friedman and Markovitch, 2015; Smith et al., 2017), are therefore not considered.”