Displaying similar documents to “Improving prediction models applied in systems monitoring natural hazards and machinery”

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...

A rainfall forecasting method using machine learning models and its application to the Fukuoka city case

S. Monira Sumi, M. Faisal Zaman, Hideo Hirose (2012)

International Journal of Applied Mathematics and Computer Science

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In the present article, an attempt is made to derive optimal data-driven machine learning methods for forecasting an average daily and monthly rainfall of the Fukuoka city in Japan. This comparative study is conducted concentrating on three aspects: modelling inputs, modelling methods and pre-processing techniques. A comparison between linear correlation analysis and average mutual information is made to find an optimal input technique. For the modelling of the rainfall, a novel hybrid...

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...

Improving surface defect detection for quality assessment of car body panels.

Christian Döring, Andreas Eichhorn, Daniela Girimonte, Rudolf Kruse (2004)

Mathware and Soft Computing

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Surface quality analysis of exterior car body panels was still character ized by manual detection of local form deviations and subjective evaluation by experts. The approach presented in this paper is based on 3-D image processing A major step towards automated quality control of produced panels is the classification of the different kinds of surface form deviations. In previous studies we compared the performance of different soft computing techniques for the detection of surface defect...