An Evolutionary-Based Approach to Learning Multiple Decision Models from Underrepresented Data

Vitaly Schetinin; Dayou Li; Carsten Maple

An Evolutionary-Based Approach to Learning Multiple Decision Models from Underrepresented Data

Artificial Intelligence 2008-05-27 v1 Neural and Evolutionary Computing

Authors: Vitaly Schetinin , Dayou Li , Carsten Maple

Abstract

The use of multiple Decision Models (DMs) enables to enhance the accuracy in decisions and at the same time allows users to evaluate the confidence in decision making. In this paper we explore the ability of multiple DMs to learn from a small amount of verified data. This becomes important when data samples are difficult to collect and verify. We propose an evolutionary-based approach to solving this problem. The proposed technique is examined on a few clinical problems presented by a small amount of data.

Keywords

machine learning theory evolutionary algorithm machine learning

Cite

@article{arxiv.0805.3800,
  title  = {An Evolutionary-Based Approach to Learning Multiple Decision Models from Underrepresented Data},
  author = {Vitaly Schetinin and Dayou Li and Carsten Maple},
  journal= {arXiv preprint arXiv:0805.3800},
  year   = {2008}
}

Comments

5 pages, 3 figures, 2 tables, The 4 th International Conference on Natural Computation (ICNC'08)

An Evolutionary-Based Approach to Learning Multiple Decision Models from Underrepresented Data

Abstract

Keywords

Cite

Comments

Related papers