Research on PMF Model Based on BP Neural Network Ensemble Learning Bagging and Fuzzy Clustering

Probability matrix factorization model can be used to solve the problem of high-dimensional sparsity of user and rating data in the recommender systems. However, most of the existing methods use the user to model the item rating, ignoring the relationship between the user and the item, so the accuracy of user-item rating prediction is still low. Therefore, this paper proposes a probabilistic matrix factorization model based on BP neural network ensemble learning, bagging, and fuzzy clustering. Firstly, the membership function of fuzzy clustering and the selection of cluster center are used to calculate the user-item rating matrix; secondly, BP neural network trains the user-item scoring matrix after clustering, further improving the accuracy of scoring prediction; finally, the bagging method in ensemble learning is introduced, which takes the number of user-item scores as the base learner, trains the base learner through BP neural network, and finally obtains the score prediction through the voting results, which improves the stability of the model. Compared with the existing PMF models, the root mean square error of the PMF model after fuzzy clustering is increased by 9.27% and 3.95%, and the average absolute error is increased by 21.14% and 1.11%, respectively; then, the performance of the first mock exam is introduced. The root mean square error of the ensemble method is increased by 4.02% and 0.42%, respectively, compared with the existing single model. Finally, the weights of BP neural network training based learner are introduced to improve the accuracy of the model, which also verifies the universality of the model.

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Detection Model of Eggs Based on Improved BP Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.564 ◽

2013 ◽

Vol 427-429 ◽

pp. 564-566

Author(s):

Jian Yang ◽

Chun Yan Xia ◽

Zhan Wu Peng

Keyword(s):

Neural Network ◽

Mean Square Error ◽

Experimental Verification ◽

Bp Neural Network ◽

Central Area ◽

Characteristic Parameter ◽

Mean Square ◽

Neural Network Ensemble ◽

Detection Model ◽

The Mean

In order to improve the precision in the detection of egg fresh degree, an improved BP neural network ensemble method was proposed herein, where the K-means clustering was applied to optimize better neural network individuals and Lagrange multiplier was used to not only compute the weight of neural network individuals, forecast egg fresh degree (Haff value). Based on the image of eggs acquired by the machine vision device, taking color characteristic parameter (H, S and I) of the central area of the egg as input and Haff value of the egg as output, a model was constructed. With experimental verification, it was calculated that the mean square error of Haff value was 1.3764 and the generalization ability of the network was high.

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An EMD-Based Neural Network Ensemble Learning Model for World Crude Oil Spot Price Forecasting

Soft Computing Applications in Business - Studies in Fuzziness and Soft Computing ◽

10.1007/978-3-540-79005-1_14 ◽

2008 ◽

pp. 261-271 ◽

Cited By ~ 9

Author(s):

Lean Yu ◽

Shouyang Wang ◽

Kin Keung Lai

Keyword(s):

Neural Network ◽

Crude Oil ◽

Ensemble Learning ◽

Learning Model ◽

Spot Price ◽

Price Forecasting ◽

Neural Network Ensemble

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A Confidence-Based RBF Neural Network Ensemble Learning Paradigm with Application to Delinquent Prediction for Credit Risk Management

Business Intelligence in Economic Forecasting ◽

10.4018/978-1-61520-629-2.ch006 ◽

2010 ◽

pp. 105-117

Author(s):

Lean Yu ◽

Shouyang Wang

Keyword(s):

Neural Network ◽

Risk Management ◽

Credit Risk ◽

Ensemble Learning ◽

Rbf Neural Network ◽

Network Models ◽

Neural Network Ensemble ◽

Credit Risk Management ◽

Learning Paradigm ◽

Neural Network Models

In this study, a multistage confidence-based radial basis function (RBF) neural network ensemble learning model is proposed to design a reliable delinquent prediction system for credit risk management. In the first stage, a bagging sampling approach is used to generate different training datasets. In the second stage, the RBF neural network models are trained using various training datasets from the previous stage. In the third stage, the trained RBF neural network models are applied to the testing dataset and some prediction results and confidence values can be obtained. In the fourth stage, the confidence values are scaled into a unit interval by logistic transformation. In the final stage, the multiple different RBF neural network models are fused to obtain the final prediction results by means of confidence measure. For illustration purpose, two publicly available credit datasets are used to verify the effectiveness of the proposed confidence-based RBF neural network ensemble learning paradigm.

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Research on Financial Early-warning based on GIHS Improved BP_AdaBoost Algorithm

MATEC Web of Conferences ◽

10.1051/matecconf/201817303004 ◽

2018 ◽

Vol 173 ◽

pp. 03004

Author(s):

Gui-fang Shen ◽

Yi-Wen Zhang

Keyword(s):

Neural Network ◽

Ensemble Learning ◽

Early Warning ◽

Bp Neural Network ◽

Learning Algorithm ◽

Adaboost Algorithm ◽

Good Point ◽

Ensemble Learning Algorithm ◽

Point Set ◽

Good Point Set

To improve the accuracy of the financial early warning of the company, aiming at defects of slow learning speed, trapped in local solution and inaccurate operating result of the traditional BP neural network with random initial weights and thresholds, a parallel ensemble learning algorithm based on improved harmony search algorithm using good point set (GIHS) optimize the BP_Adaboost is proposed. Firstly, the good-point set is used to construct a more high quality initial harmony library, and it adjusts the parameters dynamically during the search process and generates several solutions in each iteration so as to make full use of information of harmony memory to improve the global search ability and convergence speed of algorithm. Secondly, ten financial indicators are chosen as the inputs of BP neural network value, and GIHS algorithm and BP neural network are combined to construct the parallel ensemble learning algorithm to optimize BP neural network initial weights value and output threshold value. Finally, many of these weak classifier is composed as strong classifier through the AdaBoost algorithm. The improved algorithm is validated in the company's financial early warning. Simulation results show that the performance of GIHS algorithm is better than the basic HS and IHS algorithm, and the GIHS-BP_AdaBoost classifier has higher classification and prediction accuracy.

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