13,701 research outputs found
Finding Fuzzy-rough Reducts with Fuzzy Entropy
Abstract—Dataset dimensionality is undoubtedly the single most significant obstacle which exasperates any attempt to apply effective computational intelligence techniques to problem domains. In order to address this problem a technique which re-duces dimensionality is employed prior to the application of any classification learning. Such feature selection (FS) techniques attempt to select a subset of the original features of a dataset which are rich in the most useful information. The benefits can include improved data visualisation and transparency, a reduction in training and utilisation times and potentially, im-proved prediction performance. Methods based on fuzzy-rough set theory have demonstrated this with much success. Such methods have employed the dependency function which is based on the information contained in the lower approximation as an evaluation step in the FS process. This paper presents three novel feature selection techniques employing fuzzy entropy to locate fuzzy-rough reducts. This approach is compared with two other fuzzy-rough feature selection approaches which utilise other measures for the selection of subsets. I
Application of Computational Intelligence Techniques to Process Industry Problems
In the last two decades there has been a large progress in the computational
intelligence research field. The fruits of the effort spent on the research in the discussed
field are powerful techniques for pattern recognition, data mining, data modelling, etc.
These techniques achieve high performance on traditional data sets like the UCI
machine learning database. Unfortunately, this kind of data sources usually represent
clean data without any problems like data outliers, missing values, feature co-linearity,
etc. common to real-life industrial data. The presence of faulty data samples can have
very harmful effects on the models, for example if presented during the training of the
models, it can either cause sub-optimal performance of the trained model or in the worst
case destroy the so far learnt knowledge of the model. For these reasons the application
of present modelling techniques to industrial problems has developed into a research
field on its own. Based on the discussion of the properties and issues of the data and the
state-of-the-art modelling techniques in the process industry, in this paper a novel
unified approach to the development of predictive models in the process industry is
presented
Taming Wild High Dimensional Text Data with a Fuzzy Lash
The bag of words (BOW) represents a corpus in a matrix whose elements are the
frequency of words. However, each row in the matrix is a very high-dimensional
sparse vector. Dimension reduction (DR) is a popular method to address sparsity
and high-dimensionality issues. Among different strategies to develop DR
method, Unsupervised Feature Transformation (UFT) is a popular strategy to map
all words on a new basis to represent BOW. The recent increase of text data and
its challenges imply that DR area still needs new perspectives. Although a wide
range of methods based on the UFT strategy has been developed, the fuzzy
approach has not been considered for DR based on this strategy. This research
investigates the application of fuzzy clustering as a DR method based on the
UFT strategy to collapse BOW matrix to provide a lower-dimensional
representation of documents instead of the words in a corpus. The quantitative
evaluation shows that fuzzy clustering produces superior performance and
features to Principal Components Analysis (PCA) and Singular Value
Decomposition (SVD), two popular DR methods based on the UFT strategy
Nature-Inspired Adaptive Architecture for Soft Sensor Modelling
This paper gives a general overview of the challenges present in the research field of Soft Sensor
building and proposes a novel architecture for building of Soft Sensors, which copes with the identified challenges. The
architecture is inspired and making use of nature-related techniques for computational intelligence. Another aspect,
which is addressed by the proposed architecture, are the identified characteristics of the process industry data. The data
recorded in the process industry consist usually of certain amount of missing values or sample exceeding meaningful
values of the measurements, called data outliers. Other process industry data properties causing problems for the
modelling are the collinearity of the data, drifting data and the different sampling rates of the particular hardware
sensors. It is these characteristics which are the source of the need for an adaptive behaviour of Soft Sensors. The
architecture reflects this need and provides mechanisms for the adaptation and evolution of the Soft Sensor at different
levels. The adaptation capabilities are provided by maintaining a variety of rather simple models. These particular
models, called paths in terms of the architecture, can for example focus on different partition of the input data space, or
provide different adaptation speeds to changes in the data. The actual modelling techniques involved into the
architecture are data-driven computational learning approaches like artificial neural networks, principal component
regression, etc
Data-driven Soft Sensors in the Process Industry
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work
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