An Eager Regression Method Based on Selecting Appropriate Features

May 2001

Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2001)

Volume

Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2001)

All Papers

This paper describes a machine learning method, called Regression by Selecting Best Features (RSBF).RSBF consists of two phases: The first phase aims to find the predictive power of each feature attribute by constructing simple linear regression lines, one per each continuous feature and number of categories per each categorical feature. Although the predictive power of a continuous feature is constant, it varies for each distinct value of categorical features. The second phase constructs multiple linear regression lines among continuous features, each time excluding the worst feature among the current set, and constructs multiple linear regression lines. Finally, these multiple linear regression lines and categorical features simple linear regression lines are sorted according to their predictive power. In the querying phase of learning, the best linear regression line and the features constructing that line are selected to make predictions. Keywords: Prediction, Feature Projection, and Regression.