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A Contribution to the Ranking and Description of Classifications

Research Area:  Artificial Intelligence


   This thesis presents a novel and complete fuzzy multi-criteria decision making (MCDM) methodology. This methodology is specifically designed for selecting classifications in the framework of unsupervised learning systems. The main results obtained are twofold. On the one hand, the definition of fuzzy criteria to be used to assess the suitability of a set of given classifications and, on the other hand, the design and development of a natural language generation (NLG) system to qualitatively describe them. Unsupervised learning systems often produce a large number of possible classifications.
   This approach could result in classifications being discarded and not taken into account when they marginally fail to meet one particular criterion even though they meet other criteria with a high score. An alternative solution to this sequential approach has been introduced in this thesis. It consists of evaluating the degree up to which each fuzzy criterion is met by each classification and, only after this, aggregating for each classification the individual assessments.
    In addition, a NLG system to qualitatively describe the most important characteristics of the best classification is designed and developed in order to fully understand the chosen classification. Finally, this new methodology is applied to a real business problem in a marketing context. The main purpose of this application is to show how the proposed methodology can help marketing experts in the design of specific-oriented marketing strategies by means of an automatic and interpretable segmentation system.

Name of the Researcher:  Sanchez Hernandez, German

Name of the Supervisor(s):  Aguado

Year of Completion:  2013

University:  Politecnica University

Thesis Link:   Home Page Url