Michigan-style learning classifier systems (M-LCSs) represent an adaptive and powerful
class of evolutionary algorithms which distribute the learned solution over a sizable …

Focusing on credit risk modelling, this paper introduces a novel approach for ensemble
modelling based on a normative linear pooling. Models are first classified as dominant and …

This paper addresses the problem of Rule Ensemble Learning (REL), where the goal is
simultaneous discovery of a small set of simple rules and their optimal weights that lead to …

In this paper, we propose a novel approach to clustering noisy and complex data sets based
on the eXtend Classifier Systems (XCS). The proposed approach, termed XCSc, has three …

The evaluation of clustering results plays an important role in clustering analysis and usually
is completed by a validity index or several. But currently existing validity indexes are …

学习分类系统作为一种自适应的机器学习技术, 已经被成功地运用于解决多种学习问题.
传统的学习分类系统的工作主要关注监督学习(分类) 和无监督学习(聚类) 环境下的研究 …

Background A key challenge in the field of HIV-1 protein evolution is the identification of
coevolving amino acids at the molecular level. In the past decades, many sequence-based …

Self-adaptation is a key feature of evolutionary algorithms (EAs). Although EAs have been
used successfully to solve a wide variety of problems, the performance of this technique …

Rule Compaction of populations of Learning Classifier Systems (LCS) has always been a
topic of interest to get more insights into the discovered underlying patterns from the data or …

In this paper, we present a more effective approach to clustering with eXtended Classifier
System (XCS) which is divided into two phases. The first phase is the XCS learning process …