Application of the Variable Precision Rough Sets Model to Estimate the Outlier Probability of Each Element
| Autor: | Alberto Fernández Oliva, Francisco Maciá Pérez, Jose Vicente Berna Martienz, Miguel Abreu Ortega |
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| Přispěvatelé: | Universidad de Alicante. Departamento de Tecnología Informática y Computación, GrupoM. Redes y Middleware |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Multidisciplinary
General Computer Science Basis (linear algebra) Article Subject Computer science Outlier probability Rough sets theory 020206 networking & telecommunications 02 engineering and technology Variable precision rough sets computer.software_genre Field (computer science) lcsh:QA75.5-76.95 Knowledge extraction Outlier 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Anomaly detection Relevance (information retrieval) Data mining Rough set lcsh:Electronic computers. Computer science computer Arquitectura y Tecnología de Computadores Universe (mathematics) |
| Zdroj: | Complexity, Vol 2018 (2018) RUA. Repositorio Institucional de la Universidad de Alicante Universidad de Alicante (UA) |
| ISSN: | 1076-2787 |
| DOI: | 10.1155/2018/4867607 |
| Popis: | In a data mining process, outlier detection aims to use the high marginality of these elements to identify them by measuring their degree of deviation from representative patterns, thereby yielding relevant knowledge. Whereas rough sets (RS) theory has been applied to the field of knowledge discovery in databases (KDD) since its formulation in the 1980s; in recent years, outlier detection has been increasingly regarded as a KDD process with its own usefulness. The application of RS theory as a basis to characterise and detect outliers is a novel approach with great theoretical relevance and practical applicability. However, algorithms whose spatial and temporal complexity allows their application to realistic scenarios involving vast amounts of data and requiring very fast responses are difficult to develop. This study presents a theoretical framework based on a generalisation of RS theory, termed the variable precision rough sets model (VPRS), which allows the establishment of a stochastic approach to solving the problem of assessing whether a given element is an outlier within a specific universe of data. An algorithm derived from quasi-linearisation is developed based on this theoretical framework, thus enabling its application to large volumes of data. The experiments conducted demonstrate the feasibility of the proposed algorithm, whose usefulness is contextualised by comparison to different algorithms analysed in the literature. This work has been supported by University of Alicante projects GRE14-02 and Smart University. |
| Databáze: | OpenAIRE |
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