Intelligent feature selection for neural regression : techniques and applications
Zhang, Fu (2012) Intelligent feature selection for neural regression : techniques and applications. PhD thesis, University of Warwick.
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Official URL: http://webcat.warwick.ac.uk/record=b2581803~S1
Feature Selection (FS) and regression are two important technique categories in
Data Mining (DM). In general, DM refers to the analysis of observational datasets
to extract useful information and to summarise the data so that it can be more
understandable and be used more efficiently in terms of storage and processing.
FS is the technique of selecting a subset of features that are relevant to the
development of learning models. Regression is the process of modelling and
identifying the possible relationships between groups of features (variables).
Comparing with the conventional techniques, Intelligent System Techniques
(ISTs) are usually favourable due to their flexible capabilities for handling real‐life
problems and the tolerance to data imprecision, uncertainty, partial truth, etc.
This thesis introduces a novel hybrid intelligent technique, namely Sensitive
Genetic Neural Optimisation (SGNO), which is capable of reducing the
dimensionality of a dataset by identifying the most important group of features.
The capability of SGNO is evaluated with four practical applications in three
research areas, including plant science, civil engineering and economics.
SGNO is constructed using three key techniques, known as the core modules,
including Genetic Algorithm (GA), Neural Network (NN) and Sensitivity Analysis
(SA). The GA module controls the progress of the algorithm and employs the NN
module as its fitness function. The SA module quantifies the importance of each
available variable using the results generated in the GA module. The global
sensitivity scores of the variables are used determine the importance of the
variables. Variables of higher sensitivity scores are considered to be more important than the variables with lower sensitivity scores. After determining the
variables’ importance, the performance of SGNO is evaluated using the NN module
that takes various numbers of variables with the highest global sensitivity scores
as the inputs. In addition, the symbolic relationship between a group of variables
with the highest global sensitivity scores and the model output is discovered
using the Multiple‐Branch Encoded Genetic Programming (MBE‐GP).
A total of four datasets have been used to evaluate the performance of SGNO.
These datasets involve the prediction of short‐term greenhouse tomato yield,
prediction of longitudinal dispersion coefficients in natural rivers, prediction of
wave overtopping at coastal structures and the modelling of relationship between
the growth of industrial inputs and the growth of the gross industrial output.
SGNO was applied to all these datasets to explore its effectiveness of reducing the
dimensionality of the datasets. The performance of SGNO is benchmarked with
four dimensionality reduction techniques, including Backward Feature Selection
(BFS), Forward Feature Selection (FFS), Principal Component Analysis (PCA) and
Genetic Neural Mathematical Method (GNMM).
The applications of SGNO on these datasets showed that SGNO is capable of
identifying the most important feature groups of in the datasets effectively and
the general performance of SGNO is better than those benchmarking techniques.
Furthermore, the symbolic relationships discovered using MBE‐GP can generate
performance competitive to the performance of NN models in terms of regression
|Item Type:||Thesis or Dissertation (PhD)|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software|
|Library of Congress Subject Headings (LCSH):||Data mining, Artificial intelligence, Neural networks (Computer science)|
|Official Date:||May 2012|
|Institution:||University of Warwick|
|Theses Department:||School of Engineering|
|Supervisor(s)/Advisor:||Iliescu, Daciana ; Hines, Evor, 1957-|
|Extent:||266 leaves : ill., charts|
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