In this paper we propose a meta-evolutionary approach to improve on the performance of individual classifiers. In the proposed system, individual classifiers evolve, competing to ...
Ensemble methods are learning algorithms that construct a set of classi ers and then classify new data points by taking a (weighted) vote of their predictions. The original ensembl...
Driven by new regulations and animal welfare, the need to develop in silico models has increased recently as alternative approaches to safety assessment of chemicals without animal...
Monitoring cluster evolution in data streams is a major research topic in data streams mining. Previous clustering methods for evolving data streams focus on global clustering res...
Liang Tang, Chang-jie Tang, Lei Duan, Chuan Li, Ye...
Abstract. Data with multi-valued categorical attributes can cause major problems for decision trees. The high branching factor can lead to data fragmentation, where decisions have ...