I entered the world of Learning Classifier Systems (LCS) through their introduction by Will
Browne as part of a lecture series on “Advanced Artificial Intelligence” at the University of …

In this paper, a performance analysis for several well-known categorization algorithms is
made. The main goal is to find classes of algorithms based on their error rate on the whole …

A system of rule extraction out of a complex hierarchical classifier is proposed in this paper.
There are several methods for rule extraction out of trained artificial neural networks (ANN's) …

We present a study of designing compact recognizers of handwritten Chinese characters
using multiple-prototype based classifiers. A modified Quick prop algorithm is proposed to …

The number of samples that are available on the internet to train pattern classifiers is
increasing rapidly, while traditional pattern classification techniques based on a single …

This paper presents four different architectures for the hardware acceleration of axis-parallel,
oblique and non-linear decision tree ensemble classifier systems. Hardware architectures …

The nearest neighbor classifier (NNC) is a popular non-parametric classifier. It is a simple
classifier with no design phase and shows good performance. Important factors affecting the …

This paper aims to produce classifiers that are not only accurate but also interpretable to
decision makers. The classifiers are represented in the form of risk scores, ie simple linear …

The paper deals with the design and development of classifiers and, in particular, with the
problem of selecting the most relevant input variables to be used as inputs for classification …

In this paper, a new classifier design methodology, confidence-based classifier design, is
proposed to design classifiers with controlled confidence. This methodology is under the …