Abstract :
A new family of algorithm called Cline that provides a number of methods to construct and use multivariate decision trees is presented. We report experimental results for two types of data: synthetic data to visualize the behavior of the algorithms and publicly available eight data sets. The new methods have been tested against 23 other decision-tree construction algorithms based on benchmark data sets. Empirical results indicate that our approach achieves better classification accuracy compared to other algorithms.
Keywords :
"Decision trees","Machine learning algorithms","Machine learning","Pattern recognition","Data visualization","Benchmark testing","Artificial intelligence","Pattern classification","Encoding","Computational intelligence"