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An integrated two-stage methodology for optimising the accuracy of performance classification models

    Adrian Costea Affiliation
    ; Massimiliano Ferrara Affiliation
    ; Florentin Şerban Affiliation

Abstract

In this paper we propose a two-stage methodology to classify the non-banking financial institutions (NFIs) based on their financial performance. The first stage of the methodology consists of grouping the companies in similar financial performance classes (e.g.: “good”, “average”, “poor” performance classes). We optimise the allocation of the observations within the performance clusters by applying an enhanced version of an observation re-allocation procedure proposed in our previous work. Next, based on the result of the grouping phase, we construct a performance class variable by attaching a performance label to each data row. Then, in the second phase of our methodology, we propose a feed-forward neural-network classification model that maps the input space to the newly-constructed performance class variable. This model allows us to forecast the performance of new companies as data become available.

Keyword : knowledge-based systems, uncertainty modelling, applications of fuzzy sets, classification, artificial intelligence, performance evaluation, non-banking financial institutions

How to Cite
Costea, A., Ferrara, M., & Şerban, F. (2017). An integrated two-stage methodology for optimising the accuracy of performance classification models. Technological and Economic Development of Economy, 23(1), 111-139. https://doi.org/10.3846/20294913.2016.1213196
Published in Issue
Jan 22, 2017
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