Sales forecasting system based on Gray extreme learning machine with Taguchi method in retail industry

Chen, F. L.; Ou, Tsung-Yin

doi:10.1016/j.eswa.2010.07.014

Cited by 105 publications

(39 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, ELM tends to provide good generalization power at an extremely fast training speed [17], [18]. During the past several years, ELM has drawn considerable attentions in many fields related to PR [19], [20].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Lie Detection Based on F-Score and Extreme Learning Machine

et al. 2013

View full text Add to dashboard Cite

A new machine learning method referred to as F-score_ELM was proposed to classify the lying and truth-telling using the electroencephalogram (EEG) signals from 28 guilty and innocent subjects. Thirty-one features were extracted from the probe responses from these subjects. Then, a recently-developed classifier called extreme learning machine (ELM) was combined with F-score, a simple but effective feature selection method, to jointly optimize the number of the hidden nodes of ELM and the feature subset by a grid-searching training procedure. The method was compared to two classification models combining principal component analysis with back-propagation network and support vector machine classifiers. We thoroughly assessed the performance of these classification models including the training and testing time, sensitivity and specificity from the training and testing sets, as well as network size. The experimental results showed that the number of the hidden nodes can be effectively optimized by the proposed method. Also, F-score_ELM obtained the best classification accuracy and required the shortest training and testing time.

show abstract

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Lie Detection Based on F-Score and Extreme Learning Machine

et al. 2013

View full text Add to dashboard Cite

show abstract

“…Hence, ELM provides better generalization power at an efficiently faster training speed [19]. In recent decades huge attention has been achieved by ELM in various fields related to PR [20][21]. Till present, there isn't any appraisal that uses ELM for lie detection & classification of guilty & innocent subjects.…”

Section: 2extreme Learning Machine (Elm)mentioning

confidence: 99%

Study on Detection of P300 ERP Component in EEG Signals and Algorithms

Haider¹,

Jiang²,

Ashraf³

et al. 2017

Proceedings of the 3rd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 20

View full text Add to dashboard Cite

Abstract.P300 Event Related Potential (ERP) has drawn much interest in different application of psychological background. An endogenous ERP component, P300, has been widely inspected in detecting deception. Detecting P300 from EEG signals is hard to find because P300 is overloaded with noise and other EEG signals. We typically use the features of the amplitude and latency of P300 to recognize P300.In this paper we are presenting several methods for the efficient detection of P300 detection algorithms such as; reconstruction algorithm for small P300 wave using Independent Component Analysis (ICA), Extreme Learning Machine (ELM) method using BackPropagation network, Multi-domain EEG signal processing. Each detection method has good effectiveness but the complexation level is dissimilar for each algorithm based on the constraints and optimal features used at the classification stage. This survey will brings you algorithms for the P300 detection and give you detail idea about the use of them.

show abstract

“…The total variability of the S/N ratio was measured by the sum of the squared deviations from the total mean S/N ratio. Su et al [10], Chen and Ou [24], and Souza et al [12] applied P test values to proceed with the decision-making process. The P value was calculated for each design parameter.…”

Section: Anovamentioning

confidence: 99%

Integrating the Taguchi Method and Response Surface Methodology for Process Parameter Optimization of the Injection Molding

Jou¹,

Lin²,

Lee³

et al. 2014

Appl. Math. Inf. Sci.

View full text Add to dashboard Cite

Abstract:The outer coating of an optical fiber cable protects the optical fiber, and the contraction rate of the outer coating can affect the communication capacity and distance, so the contraction rate is often regarded as the key factor in defining the quality of an optical fiber cable. For this reason, using the example of the circular single-pin optical fiber cable manufacturing process of the largest optical communication manufacturer in the world, the Taguchi Method (TM) is used to screen the variables that have significant effects on the contraction rate of the outer coating. Furthermore, the optimization engineering of Response Surface Methodology (RSM) is utilized for the empirical research to acquire a prediction model that can be used to optimize the optical fiber outer coating injection molding process. The research results show that the contraction rate predicted by the integration of the Taguchi Method and RSM is 2.28%. The empirical results reveal that the contraction rate of the outer coating indeed decreased from the original 3.83% down to 2.16%, indicating that the integration of the Taguchi Method and RSM could effectively improve the quality.

show abstract

Sales forecasting system based on Gray extreme learning machine with Taguchi method in retail industry

Cited by 105 publications

References 21 publications

A Novel Approach for Lie Detection Based on F-Score and Extreme Learning Machine

A Novel Approach for Lie Detection Based on F-Score and Extreme Learning Machine

Study on Detection of P300 ERP Component in EEG Signals and Algorithms

Integrating the Taguchi Method and Response Surface Methodology for Process Parameter Optimization of the Injection Molding

Contact Info

Product

Resources

About