scholarly journals Multiple Classifiers-Assisted Evolutionary Algorithm Based on Decomposition for High-Dimensional Multi-Objective Problems

2021 ◽  
Author(s):  
Takumi Sonoda ◽  
Masaya Nakata
Keyword(s):  
Evolutionary Algorithm ◽  
High Dimensional ◽  
Support Vector ◽  
Computationally Efficient ◽  
Multi Objective ◽  
Multiple Classifiers ◽  
Training Samples ◽  
Expensive Problems ◽  
Low Dimensional ◽  
Expensive Optimization

Surrogate-assisted multi-objective evolutionary algorithms have advanced the field of computationally expensive optimization, but their progress is often restricted to low-dimensional problems. This manuscript presents a multiple classifiers-assisted evolutionary algorithm based on decomposition, which is adapted for high-dimensional expensive problems in terms of the following two insights. Compared to approximation-based surrogates, the accuracy of classification-based surrogates is robust for few high-dimensional training samples. Further, multiple local classifiers can hedge the risk of over-fitting issues. Accordingly, the proposed algorithm builds multiple classifiers with support vector machines on a decomposition-based multi-objective algorithm, wherein each local classifier is trained for a corresponding scalarization function. Experimental results statistically confirm that the proposed algorithm is competitive to the state-of-the-art algorithms and computationally efficient as well.

scholarly journals Multiple Classifiers-Assisted Evolutionary Algorithm Based on Decomposition for High-Dimensional Multi-Objective Problems

2021 ◽  
Author(s):  
Takumi Sonoda ◽  
Masaya Nakata
Keyword(s):  
Evolutionary Algorithm ◽  
High Dimensional ◽  
Support Vector ◽  
Computationally Efficient ◽  
Multi Objective ◽  
Multiple Classifiers ◽  
Training Samples ◽  
Expensive Problems ◽  
Low Dimensional ◽  
Expensive Optimization

Surrogate-assisted multi-objective evolutionary algorithms have advanced the field of computationally expensive optimization, but their progress is often restricted to low-dimensional problems. This manuscript presents a multiple classifiers-assisted evolutionary algorithm based on decomposition, which is adapted for high-dimensional expensive problems in terms of the following two insights. Compared to approximation-based surrogates, the accuracy of classification-based surrogates is robust for few high-dimensional training samples. Further, multiple local classifiers can hedge the risk of over-fitting issues. Accordingly, the proposed algorithm builds multiple classifiers with support vector machines on a decomposition-based multi-objective algorithm, wherein each local classifier is trained for a corresponding scalarization function. Experimental results statistically confirm that the proposed algorithm is competitive to the state-of-the-art algorithms and computationally efficient as well.

scholarly journals Enhancing hierarchical surrogate-assisted evolutionary algorithm for high-dimensional expensive optimization via random projection

2021 ◽  
Author(s):  
Xiaodong Ren ◽  
Daofu Guo ◽  
Zhigang Ren ◽  
Yongsheng Liang ◽  
An Chen
Keyword(s):  
Optimization Problems ◽  
Solution Space ◽  
Random Projection ◽  
Surrogate Models ◽  
High Dimensional ◽  
Local Models ◽  
Dimensional Solution ◽  
Training Samples ◽  
Low Dimensional ◽  
Expensive Optimization

AbstractBy remarkably reducing real fitness evaluations, surrogate-assisted evolutionary algorithms (SAEAs), especially hierarchical SAEAs, have been shown to be effective in solving computationally expensive optimization problems. The success of hierarchical SAEAs mainly profits from the potential benefit of their global surrogate models known as “blessing of uncertainty” and the high accuracy of local models. However, their performance leaves room for improvement on high-dimensional problems since now it is still challenging to build accurate enough local models due to the huge solution space. Directing against this issue, this study proposes a new hierarchical SAEA by training local surrogate models with the help of the random projection technique. Instead of executing training in the original high-dimensional solution space, the new algorithm first randomly projects training samples onto a set of low-dimensional subspaces, then trains a surrogate model in each subspace, and finally achieves evaluations of candidate solutions by averaging the resulting models. Experimental results on seven benchmark functions of 100 and 200 dimensions demonstrate that random projection can significantly improve the accuracy of local surrogate models and the new proposed hierarchical SAEA possesses an obvious edge over state-of-the-art SAEAs.

scholarly journals High Dimensional Feature for Hyperspectral Image Classification

2018 ◽  
Vol 246 ◽  
pp. 03041
Author(s):  
Cailing Wang ◽  
Hongwei Wang ◽  
Yinyong Zhang ◽  
Jia Wen ◽  
Fan Yang
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Texture Feature ◽  
High Dimensional ◽  
Support Vector ◽  
Hyperspectral Image Classification ◽  
Feature Based ◽  
Pca Method ◽  
The One ◽  
Low Dimensional

Making a high dimensional (e.g., 100k-dim) feature for hyperspectral image classification seems not a good idea because it will bring difficulties on consequent training, computation, and storage. In this paper, we study the performance of a high-dimensional feature by texture feature. The texture feature based on multi-local binary pattern descriptor, can achieve significant improvements over both its tradition version and the one we proposed in our previous work. We also make the high-dimensional feature practical, we employ the PCA method for dimension reduction and support vector machine for hyperspectral image classification. The two real hyperspectral image datasets are employed. Our experimental results with real hyperspectral images indicate that the high dimensional feature can enhance the classification accuracy than some low dimensional.

scholarly journals An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning

2019 ◽  
Vol 9 (18) ◽  
pp. 3723
Author(s):  
Sharif ◽  
Mumtaz ◽  
Shafiq ◽  
Riaz ◽  
Ali ◽  
...  
Keyword(s):  
Machine Learning ◽  
Research Work ◽  
Principal Component ◽  
Research Area ◽  
Ensemble Classification ◽  
High Dimensional ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
N Gram ◽  
Low Dimensional

The rise of social media has led to an increasing online cyber-war via hate and violent comments or speeches, and even slick videos that lead to the promotion of extremism and radicalization. An analysis to sense cyber-extreme content from microblogging sites, specifically Twitter, is a challenging, and an evolving research area since it poses several challenges owing short, noisy, context-dependent, and dynamic nature content. The related tweets were crawled using query words and then carefully labelled into two classes: Extreme (having two sub-classes: pro-Afghanistan government and pro-Taliban) and Neutral. An Exploratory Data Analysis (EDA) using Principal Component Analysis (PCA), was performed for tweets data (having Term Frequency—Inverse Document Frequency (TF-IDF) features) to reduce a high-dimensional data space into a low-dimensional (usually 2-D or 3-D) space. PCA-based visualization has shown better cluster separation between two classes (extreme and neutral), whereas cluster separation, within sub-classes of extreme class, was not clear. The paper also discusses the pros and cons of applying PCA as an EDA in the context of textual data that is usually represented by a high-dimensional feature set. Furthermore, the classification algorithms like naïve Bayes’, K Nearest Neighbors (KNN), random forest, Support Vector Machine (SVM) and ensemble classification methods (with bagging and boosting), etc., were applied with PCA-based reduced features and with a complete set of features (TF-IDF features extracted from n-gram terms in the tweets). The analysis has shown that an SVM demonstrated an average accuracy of 84% compared with other classification models. It is pertinent to mention that this is the novel reported research work in the context of Afghanistan war zone for Twitter content analysis using machine learning methods.

scholarly journals Surrogate-assisted evolutionary algorithm for expensive constrained multi-objective discrete optimization problems

2021 ◽  
Author(s):  
Qinghua Gu ◽  
Qian Wang ◽  
Neal N. Xiong ◽  
Song Jiang ◽  
Lu Chen
Keyword(s):  
Evolutionary Algorithm ◽  
Discrete Optimization ◽  
Optimization Problems ◽  
Benchmark Problems ◽  
Multi Objective ◽  
Random Forest Models ◽  
Probability Operator ◽  
Expensive Optimization ◽  
Optimization Efficiency ◽  
Discrete Optimization Problems

AbstractSurrogate-assisted optimization has attracted much attention due to its superiority in solving expensive optimization problems. However, relatively little work has been dedicated to addressing expensive constrained multi-objective discrete optimization problems although there are many such problems in the real world. Hence, a surrogate-assisted evolutionary algorithm is proposed in this paper for this kind of problem. Specifically, random forest models are embedded in the framework of the evolutionary algorithm as surrogates to improve approximate accuracy for discrete optimization problems. To enhance the optimization efficiency, an improved stochastic ranking strategy based on the fitness mechanism and adaptive probability operator is presented, which also takes into account both convergence and diversity to advance the quality of candidate solutions. To validate the proposed algorithm, it is comprehensively compared with several well-known optimization algorithms on several benchmark problems. Numerical experiments are demonstrated that the proposed algorithm is very promising for the expensive constrained multi-objective discrete optimization problems.

Soft Sensing Based on Kernel Isomap

2013 ◽  
Vol 278-280 ◽  
pp. 1349-1352
Author(s):  
Qiang Wang ◽  
Xue Min Tian
Keyword(s):  
Diesel Oil ◽  
Dimensional Structure ◽  
High Dimensional ◽  
Support Vector ◽  
Soft Sensing ◽  
Regression Modelling ◽  
Vector Machines ◽  
Novel Method ◽  
Solidifying Point ◽  
Low Dimensional

A novel method of soft sensing is propsed combined Kernel Isomap (KIsomap) with Least squares support vector machines (LS-SVM). KIsomap is an improved Isomap and has a generalization property by utilizing kernel trick. It is a kind of novelly promoted nonlinear methods for dimension reduction, and can effectively find out the intrinsic low dimensional structure from high dimensional data. The KIsomap is used to feature extraction and reduce dimensions of sample. The LSSVM is applied to proceed regression modelling, which can not only reduce the complexity of modeling but also improve the generalization ability.The proposed method is used to build soft sensing of diesel oil solidifying point. Compared with other two models, the result shows that KIsomap-LSSVM approach is effective and correct.

Sign in / Sign up

Export Citation Format

Share Document