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

TL;DR

This paper introduces an evolutionary approach for training multiple decision models effectively from limited, underrepresented data, improving decision accuracy and confidence evaluation in critical applications.

Contribution

It presents a novel evolutionary-based method for learning multiple decision models from small datasets, addressing challenges in data scarcity and verification.

Findings

Effective on clinical problems with limited data

Improves decision accuracy and confidence assessment

Demonstrates viability of evolutionary methods in data-scarce scenarios

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.