---
title: "Flexibly Exploiting Prior Knowledge in Empirical Learning"
authors:
  - "Julio Ortega"
  - "Douglas Fisher"
doi: ""
url: "https://ijcai.org/proceedings/1995/135"
pdf: "https://ijcai.org/proceedings/1995/0135.pdf"
venue: "Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence"
year: 1995
pages: "1041-1048"
track: "Learning"
keywords:
---

# Flexibly Exploiting Prior Knowledge in Empirical Learning

Julio Ortega, Douglas Fisher

*Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence*, pages 1041-1048.

## Abstract

This paper presents a method to incorporate knowledge from possibly imperfect models and domain theories into inductive learning of decision trees for classification. The approach assumes that a mode l or domain theory reflects useful prior knowledge of the task. Thus , the default bias should accept the model's predictions as accurate, even in the face of somewhat cont radictory data, which may be unrepresentative or noisy. However , our approach allows the system to a bandon the model or domain theory, or portions thereof, in the face of sufficiently contradictory data . In particular, we use C4 .5 to induce decision t rees from data that hav e been augmented by model or domaint heory-derived 'features ' . We weakly bias the system to select model-derived features during decision tree induction, but this preference is not dogmatically applied. Our experiments vary imperfection in a model, the representativeness of data , and th e veracity with which model-derived f eat ures are preferred .