Aggregating classifiers with mathematical programming

Aggregating classifiers with ordinal response structure

Mathematical solution of multilevel fractional programming problem with fuzzy goal programming approach

In this paper, we show a procedure for solving multilevel fractional programming problems in a large hierarchical decentralized organization using fuzzy goal programming approach. In the proposed method, the tolerance membership functions for the fuzzily described numerator and denominator part of the objective functions of all levels as well as the control vectors of the higher level decision ...

Evolving Text Classifiers with Genetic Programming

We describe a method for using Genetic Programming (GP) to evolve document classifiers. GP’s create regular expression type specifications consisting of particular sequences and patterns of N-Grams (character strings) and acquire fitness by producing expressions, which match documents in a particular category but do not match documents in any other category. Libraries of N-Gram patterns have be...

Evolving genetic programming classifiers with novelty search

Novelty Search (NS) is a unique approach towards search and optimization, where an explicit objective function is replaced by a measure of solution novelty. However, NS has been mostly used in evolutionary robotics while its usefulness in classic machine learning problems has not been explored. This work presents a NS-based genetic programming (GP) algorithm for supervised classification. Resul...

Non-Disjoint Discretization for Aggregating One-Dependence Estimator Classifiers

There is still lack of clarity about the best manner in which to handle numeric attributes when applying Bayesian network classifiers. Discretization methods entail an unavoidable loss of information. Nonetheless, a number of studies have shown that appropriate discretization can outperform straightforward use of common, but often unrealistic parametric distribution (e.g. Gaussian). Previous st...