Combination of multiple classifiers for the customer's purchase behavior prediction

Kim, Eunju; Kim, Wooju; Lee, Yillbyung

doi:10.1016/s0167-9236(02)00079-9

Cited by 144 publications

(81 citation statements)

References 6 publications

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“…However, one of the significant limitations of Bayesian method is that it requires mutual independencies among multiple classifiers which doesn't usually hold in real application [22]. decisions of the current input, is called the focal point.…”

Section: Bayesian Belief Methodmentioning

confidence: 99%

Multi-agent decision fusion for motor fault diagnosis

Niu

Han

Yang

et al. 2007

Mechanical Systems and Signal Processing

126

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approach is required to generate better results. This paper proposes a decision fusion system for fault diagnosis, which integrates data sources of different types of sensors and decisions of multiple classifiers.First, non-commensurate sensors data sets are combined using an improved sensor fusion method at a decision-level by using relativity theory. The generated decision vectors are then selected based on correlation measure of classifiers in order to find an optimal sequence of classifiers fusion, which can lead to the best fusion performance. Finally, multi-agent classifiers fusion algorithm is employed as the core of the whole fault diagnosis system. The efficiency of the proposed system was demonstrated through fault diagnosis of induction motors. The experimental results show that this system can lead to super performance when compared with the best individual classifier with single source data.

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Section: Bayesian Belief Methodmentioning

confidence: 99%

Multi-agent decision fusion for motor fault diagnosis

Niu

Han

Yang

et al. 2007

Mechanical Systems and Signal Processing

126

View full text Add to dashboard Cite

show abstract

“…Most of the prior literature chose well-known combined methods such as equal weighted, majority voting; Borda count; and Bayesian or intelligent algorithms, such as the neural network and fuzzy algorithm, as the combination method [25,37]. Nevertheless, the existing combination methods listed above have their own limitations [25].…”

Section: Brief Review Of Combined Forecasting Models For Bfpmentioning

confidence: 99%

Soft Set Theory Oriented Forecast Combination Method for Business Failure Prediction

2015

J Inf Process Syst

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This paper presents a new combined forecasting method that is guided by the soft set theory (CFBSS) to predict business failures with different sample sizes. The proposed method combines both qualitative analysis and quantitative analysis to improve forecasting performance. We considered an expert system (ES), logistic regression (LR), and support vector machine (SVM) as forecasting components whose weights are determined by the receiver operating characteristic (ROC) curve. The proposed procedure was applied to real data sets from Chinese listed firms. For performance comparison, single ES, LR, and SVM methods, the combined forecasting method based on equal weights (CFBEWs), the combined forecasting method based on neural networks (CFBNNs), and the combined forecasting method based on rough sets and the D-S theory (CFBRSDS) were also included in the empirical experiment. CFBSS obtains the highest forecasting accuracy and the second-best forecasting stability. The empirical results demonstrate the superior forecasting performance of our method in terms of accuracy and stability.

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“…In this regard, there are some studies about GA application in feature selection. Combining multiple classifier based on genetic algorithm [45], using GA in input variable selection [46], applying GA in variable selection with customer clustering [47] and use of GA to combine feature selection methods [48]. Furthermore, there are some studies about using GA to build decision trees that are provided accordingly.…”

Section: Literature Reviewmentioning

confidence: 99%