A distributed PSO–SVM hybrid system with feature selection and parameter optimization

Huang, Cheng-Lung; Dun, Jian-Fan

doi:10.1016/j.asoc.2007.10.007

Cited by 495 publications

(213 citation statements)

References 15 publications

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“…In formula, X t as time t raw data value, X t represents the normalized value of t moment data, distributed in the range [0,1]…”

Section: Energy Consumption Data Normalization Research On Energy Savmentioning

confidence: 99%

“…Literature [1], in view of the existing support vector machine model, the accuracy of the parameter selection problem, proposed a parameter optimization method based on particle swarm optimization, for solving super parameter search issues beyond the scope, and the logarithmic scale parameters to further improve the search efficiency of particle swarm optimization method; Literature [2], this paper presents a particle swarm algorithm and gradient descent method algorithm combining the local minima in the research of energy value problems by using gradient descent method instead of the original algorithm, improving the convergence speed, improved accuracy and efficiency; Literature [3], based on the gradient descent of improved wavelet neural network parameter optimization in local minima and oscillation effect shortcomings of the algorithm, that solve the general mathematical model is hard to describe the energy consumption in the process of multi-factor quantitative analysis is feasible for gradient descent method.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research on Smart Home Energy Management Algorithm Based on Cloud Environment

Sun¹,

Ren²,

Ye³

et al. 2017

dtcse

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Abstract. Energy saving algorithm for smart home energy consumption budget optimization under the SVM model using a simple particle swarm optimization algorithm to find the optimal value caused by the slow speed gradient value based on PSO algorithm is proposed to optimize the use of machine learning algorithm GDPSO algorithm (Gradient Descent based on down Particle Swarm Optimization Algorithm). First of all, the establishment of energy structure and energy consumption model of optimal hyperplane selection of penalty factor and Gauss the appropriate parameters; secondly, parameter selection and optimization of energy consumption model for smart home energy-saving emission reduction requirements using GDPSO algorithm, and improves the efficiency of the optimal SVM parameters to improve the accuracy of solution. A simulation example shows the effectiveness of the algorithm.

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“…In formula, X t as time t raw data value, X t represents the normalized value of t moment data, distributed in the range [0,1]…”

Section: Energy Consumption Data Normalization Research On Energy Savmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Smart Home Energy Management Algorithm Based on Cloud Environment

Sun¹,

Ren²,

Ye³

et al. 2017

dtcse

View full text Add to dashboard Cite

show abstract

“…The PSO algorithm was developed for the optimization of functions that are not continuously linear. The support vector machine used in the study was optimized by the particle swarm algorithm [44][45][46].…”

Section: Classification Phasementioning

confidence: 99%

Computer-aided detection of lung nodules using outer surface features

Demir

Camurcu

2015

BME

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Abstract. In this study, a computer-aided detection (CAD) system was developed for the detection of lung nodules in computed tomography images. The CAD system consists of four phases, including two-dimensional and three-dimensional preprocessing phases. In the feature extraction phase, four different groups of features are extracted from volume of interests: morphological features, statistical and histogram features, statistical and histogram features of outer surface, and texture features of outer surface. The support vector machine algorithm is optimized using particle swarm optimization for classification. The CAD system provides 97.37% sensitivity, 86.38% selectivity, 88.97% accuracy and 2.7 false positive per scan using three groups of classification features. After the inclusion of outer surface texture features, classification results of the CAD system reaches 98.03% sensitivity, 87.71% selectivity, 90.12% accuracy and 2.45 false positive per scan. Experimental results demonstrate that outer surface texture features of nodule candidates are useful to increase sensitivity and decrease the number of false positives in the detection of lung nodules in computed tomography images.

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“…In other words, the filter approach is simpler and faster than the wrapper approach. Some of the feature selection methods using filter approach, among others, are gain ratio (Karegowda et al, 2010, Priyadarsini et al, 2011, and Anggraeny et al, 2013, particle swarm intelligence (PSO) (Yang et al, 2007 andHuang et al, 2008), and differential evolution (Khushaba et al, 2011). Some feature selection methods using wrapper approach are ant colony optimization (ACO) (Kanan et al, 2008) and sequential floating forward selection (SFFS) (Liao et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Modification of Hidden Layer Weight in Extreme Learning Machine Using Gain Ratio

Anggraeny¹,

Purbasari²

2016

MATEC Web of Conferences

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Extreme Learning Machine (ELM) is a method of learning feed forward neural network quickly and has a fairly good accuracy. This method is devoted to a feed forward neural network with one hidden layer where the parameters (i.e. weight and bias) are adjusted one time randomly at the beginning of the learning process. In neural network, the input layer is connected to all characteristics/features, and the output layer is connected to all classes of species. This research used three datasets from UCI database, which were Iris, Breast Wisconsin, and Dermatology, with each dataset having several features. Each characteristic/feature of the data has a role in the process of classification levels, starting from the most influencing role to non-influencing at all. Gain ratio was used to extract each feature role on each datasets. Gain ratio is a method to extract feature role in order to develop a decision tree structure. In this study, ELM structure has been modified, where the random weights of the hidden layer were adjusted to the level of each feature role in determining the species class, so as to improve the level of training and testing accuracy. The proposed method has higher classification accuracy rate than basic ELM on all three datasets, which were 99%, 96%, and 82%, respectively.

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A distributed PSO–SVM hybrid system with feature selection and parameter optimization

Cited by 495 publications

References 15 publications

Research on Smart Home Energy Management Algorithm Based on Cloud Environment

Research on Smart Home Energy Management Algorithm Based on Cloud Environment

Computer-aided detection of lung nodules using outer surface features

Modification of Hidden Layer Weight in Extreme Learning Machine Using Gain Ratio

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