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What is Learning by Conceptual Classification

Handbook of Research on Decision Sciences and Applications in the Transportation Sector
From a series of individual descriptions of objects, the goal is to build groupings into conceptual classes organized (for example in a hierarchical structure) and characterized in such a way as to satisfy a fixed quality criterion. The objective is not only to increase the precision of the classification (by increasing the intra-class similarity and by reducing the inter-class similarity), but also to guarantee the evolutionary character of the classification (the grouping by classes must be able to be reconfigured dynamically).
Published in Chapter:
Decision Support Approach for Assessing of Rail Transport: Methods Based on AI and Machine Learning
Habib Hadj-Mabrouk (University Gustave Eiffel, France)
DOI: 10.4018/978-1-7998-8040-0.ch005
Abstract
As part of the certification process for a new rail transport system, one of the essential steps is to examine, from a safety point of view, that the system (and its technical and human environment) satisfies the regulatory requirements of safety and in particular that it can in no case be a source of risk for travelers. This intellectual process by which a certification expert assesses a situation, predicts an event, or makes a decision is often difficult to model in the form of reliable and definitive algorithms. This difficulty can be partially overcome by using artificial intelligence (AI) techniques. In order to improve the usual approaches to analysis and evaluation of safety studies used in the context of certification, this chapter presents several methods and tools based on AI techniques and in particular on knowledge acquisition methods, supervised machine learning (SML), case-based reasoning (CBR), and knowledge-based systems (KBS).
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