Displaying similar documents to “Properties and pre-processing strategies to enhance the discovery of functional dependency with degree of satisfaction”

DFIS: A novel data filling approach for an incomplete soft set

Hongwu Qin, Xiuqin Ma, Tutut Herawan, Jasni Mohamad Zain (2012)

International Journal of Applied Mathematics and Computer Science

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The research on incomplete soft sets is an integral part of the research on soft sets and has been initiated recently. However, the existing approach for dealing with incomplete soft sets is only applicable to decision making and has low forecasting accuracy. In order to solve these problems, in this paper we propose a novel data filling approach for incomplete soft sets. The missing data are filled in terms of the association degree between the parameters when a stronger association...

Rough set-based dimensionality reduction for supervised and unsupervised learning

Qiang Shen, Alexios Chouchoulas (2001)

International Journal of Applied Mathematics and Computer Science

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The curse of dimensionality is a damning factor for numerous potentially powerful machine learning techniques. Widely approved and otherwise elegant methodologies used for a number of different tasks ranging from classification to function approximation exhibit relatively high computational complexity with respect to dimensionality. This limits severely the applicability of such techniques to real world problems. Rough set theory is a formal methodology that can be employed to reduce...

Using genetic feature selection for optimizing user profiles.

Henrik Legind Larsen, Nicolás Marín, María José Martín-Bautista, M. Amparo Vila (2000)

Mathware and Soft Computing

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Most of the techniques used in text classification are determined by the occurrences of the words (terms) appearing in the documents, combined with the user feedback over the documents retrieved. However, in our model, the most relevant terms will be selected from a previous fuzzy classification given by the genetic algorithm guided by the user feedback, but using techniques from Machine Learning. A feature selection process is carried out through a Genetic Algorithm in order to find...

Neuro-rough-fuzzy approach for regression modelling from missing data

Krzysztof Simiński (2012)

International Journal of Applied Mathematics and Computer Science

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Real life data sets often suffer from missing data. The neuro-rough-fuzzy systems proposed hitherto often cannot handle such situations. The paper presents a neuro-fuzzy system for data sets with missing values. The proposed solution is a complete neuro-fuzzy system. The system creates a rough fuzzy model from presented data (both full and with missing values) and is able to elaborate the answer for full and missing data examples. The paper also describes the dedicated clustering algorithm....

ReSySTER: A hybrid recommender system for Scrum team roles based on fuzzy and rough sets

Ricardo Colomo-Palacios, Israel González-Carrasco, José Luis López-Cuadrado, Ángel García-Crespo (2012)

International Journal of Applied Mathematics and Computer Science

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Agile development is a crucial issue within software engineering because one of the goals of any project leader is to increase the speed and flexibility in the development of new commercial products. In this sense, project managers must find the best resource configuration for each of the work packages necessary for the management of software development processes in order to keep the team motivated and committed to the project and to improve productivity and quality. This paper presents...