A New Incremental Instance-Based Learning Using Recursive Partitioning

Data Reduction for Instance-Based Learning Using Entropy-Based Partitioning

Instance-based learning methods such as the nearest neighbor classifier have proven to perform well in pattern classification in several fields. Despite their high classification accuracy, they suffer from a high storage requirement, computational cost, and sensitivity to noise. In this paper, we present a data reduction method for instance-based learning, based on entropy-based partitioning an...

Model-based Recursive Partitioning

Recursive partitioning is embedded into the general and well-established class of parametric models that can be fitted using M-type estimators (including maximum likelihood). An algorithm for model-based recursive partitioning is suggested for which the basic steps are: (1) fit a parametric model to a data set, (2) test for parameter instability over a set of partitioning variables, (3) if ther...

Incremental Learning with Partial Instance Memory

Weighted Instance-Based Learning Using Representative Intervals

Instance-based learning algorithms are widely used due to their capacity to approximate complex target functions; however, the performance of this kind of algorithms degrades significantly in the presence of irrelevant features. This paper introduces a new noise tolerant instance-based learning algorithm, called WIB-K, that uses one or more weights, per feature per class, to classify integer-va...

Supervised Learning Using Instance-based Patterns

This paper introduces a new classification algorithm of the instance-based learning type. Training records are converted into patterns associated with a known class label, and stored permanently into a trie1-like tree structure along with other helpful information. Classifying new records is done selecting from the trie two best patterns as solutions hypotheses. Best pattern selection is done u...