| Resumen |
Abstract: The Lernmatrix is a classic associative memory model. The Lernmatrix is capable of
executing the pattern classification task, but its performance is not competitive when compared
to state-of-the-art classifiers. The main contribution of this paper consists of the proposal of a
simple mathematical transform, whose application eliminates the subtractive alterations between
patterns. As a consequence, the Lernmatrix performance is significantly improved. To perform
the experiments, we selected 20 datasets that are challenging for any classifier, as they exhibit class
imbalance. The effectiveness of our proposal was compared against seven supervised classifiers of
the most important approaches (Bayes, nearest neighbors, decision trees, logistic function, support
vector machines, and neural networks). By choosing balanced accuracy as a performance measure,
our proposal obtained the best results in 10 datasets. The elimination of subtractive alterations makes
the new model competitive against the best classifiers, and sometimes beats them. After applying the
Friedman test and the Holm post hoc test, we can conclude that within a 95% confidence, our proposal
competes successfully with the most effective classifiers of the state of the art |
