Feature Construction and Dimension Reduction Using Genetic Programming

Neshatian, Kourosh; Zhang, Mengjie; Johnston, Mark

doi:10.1007/978-3-540-76928-6_18

Cited by 37 publications

(23 citation statements)

References 9 publications

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“…Although augmented feature sets (datasets) are supersets of constructed features, previous research has shown that in majority of cases, the classification performance using only constructed features is higher than those using the augmented sets [63]. Besides, due to the larger number of features in the augmented datasets, there is a slight increase in dimensionality.…”

Section: A Existing Issuesmentioning

confidence: 97%

“…If the distribution of a class c along a feature X is normal, the interval I c = (μ c − 3σ c , μ c + 3σ c ) can cover 99% of instances of the class where μ c = E(X|C = c) is the mean and σ c = E(X 2 |C = c) − E 2 (X|C = c) is the standard deviation of the class distribution. In [63], it is assumed that the conditional density of a (constructed) feature is normal. The assumption is made without actually implementing a test for goodness of fit as it is infeasible to have such a test in each fitness evaluation.…”

Section: Finding the Interval Of A Classmentioning

confidence: 99%

See 1 more Smart Citation

A Filter Approach to Multiple Feature Construction for Symbolic Learning Classifiers Using Genetic Programming

Neshatian

Zhang

Andreae

2012

IEEE Trans. Evol. Computat.

Self Cite

146

116

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Feature construction is an effort to transform the input space of classification problems in order to improve the classification performance. Feature construction is particularly important for classifier inducers that cannot transform their input space intrinsically. This paper proposes GPMFC, a multiplefeature construction system for classification problems using genetic programming (GP). This paper takes a nonwrapper approach by introducing a filter-based measure of goodness for constructed features. The constructed, high-level features are functions of original input features. These functions are evolved by GP using an entropy-based fitness function that maximizes the purity of class intervals. A decomposable objective function is proposed so that the system is able to construct multiple high-level features for each problem. The constructed features are used to transform the original input space to a new space with better separability. Extensive experiments are conducted on a number of benchmark problems and symbolic learning classifiers. The results show that, in most cases, the new approach is highly effective in increasing the classification performance in rule-based and decision tree classifiers. The constructed features help improve the learning performance of symbolic learners. The constructed features, however, may lack intelligibility.

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Section: A Existing Issuesmentioning

confidence: 97%

Section: Finding the Interval Of A Classmentioning

confidence: 99%

A Filter Approach to Multiple Feature Construction for Symbolic Learning Classifiers Using Genetic Programming

Neshatian

Zhang

Andreae

2012

IEEE Trans. Evol. Computat.

Self Cite

146

116

View full text Add to dashboard Cite

show abstract

“…Variable selection based on genetic programming has been exploited in various applications where the significant inputs are generally unknown (for examples see [15,8,7,11,9,3,2]). …”

Section: Variable Selectionmentioning

confidence: 99%

Knowledge mining with genetic programming methods for variable selection in flavor design

Vladislavleva

Veeramachaneni

Burland³

et al. 2010

Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation

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This paper presents a novel approach for knowledge mining from a sparse and repeated measures dataset. Genetic programming based symbolic regression is employed to generate multiple models that provide alternate explanations of the data. This set of models, called an ensemble, is generated for each of the repeated measures separately. These multiple ensembles are then utilized to generate information about, (a) which variables are important in each ensemble, (b) cluster the ensembles into different groups that have similar variables that drive their response variable, and (c) measure sensitivity of response with respect to the important variables. We apply our methodology to a sensory science dataset. The data contains hedonic evaluations (liking scores), assigned by a diverse set of human testers, for a small set of flavors composed from seven ingredients. Our approach: (1) identifies the important ingredients that drive the liking score of a panelist and (2) segments the panelists into groups that are driven by the same ingredient, and (3) enables flavor scientists to perform the sensitivity analysis of liking scores relative to changes in the levels of important ingredients.

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“…One of the unique capabilities of genetic programming is its built-in power to select significant variables and gradually omit the variables that are not relevant while evolving models. Variable selection based on genetic programming has been exploited in various applications where the significant inputs are generally unknown (for examples see [36,15,21,23,26,36]). …”

Section: Variable Importancementioning

confidence: 99%

Knowledge mining sensory evaluation data: genetic programming, statistical techniques, and swarm optimization

Veeramachaneni¹,

Vladislavleva²,

O’Reilly³

2012

Genet Program Evolvable Mach

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Knowledge mining sensory evaluation data is a challenging process due to extreme sparsity of the data, and a large variation in responses from different members (called assessors) of the panel. The main goals of knowledge mining in sensory sciences are understanding the dependency of the perceived liking score on the concentration levels of flavors' ingredients, identifying ingredients that drive liking, segmenting the panel into groups with similar liking preferences and optimizing flavors to maximize liking per group. Our approach employs (1) Genetic programming (symbolic regression) and ensemble methods to generate multiple diverse explanations of assessor liking preferences with confidence information; (2) statistical techniques to extrapolate using the produced ensembles to unobserved regions of the flavor space, and segment the assessors into groups which either have the same propensity to like flavors, or are driven by the same ingredients; and (3) two-objective swarm optimization to identify flavors which are well and consistently liked by a selected segment of assessors.

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Feature Construction and Dimension Reduction Using Genetic Programming

Cited by 37 publications

References 9 publications

A Filter Approach to Multiple Feature Construction for Symbolic Learning Classifiers Using Genetic Programming

A Filter Approach to Multiple Feature Construction for Symbolic Learning Classifiers Using Genetic Programming

Knowledge mining with genetic programming methods for variable selection in flavor design

Knowledge mining sensory evaluation data: genetic programming, statistical techniques, and swarm optimization

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