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A New Machine Learning Technique Based on Straight Line Segments

This paper presents a new supervised machine learning technique based on distances between points and straight lines segments. Basically, given a training data set, this technique estimates a function where its value is calculated using the distance between points and two sets of straight line segments. A training algorithm has been developed to find these sets of straight line segments that minimize the mean square error. This technique has been applied on two real pattern recognition problems: (1) breast cancer data set to classify tumors as benign or malignant; (2) wine data set to classify wines in one of the three different cultivators from which they could be derived. This technique was also tested with two artificial data sets in order to show its ability to solve approximation function problems. The obtained results show that this technique has a good performance in all of these problems and they indicate that it is a good candidate to be used in machine learning applications