Robust Factorization Machines for Credit Default Prediction

Ni, Weijian; Liu, Tong; Zeng, Qingtian; Zhang, Xianke; Duan, Hua; Xie, Naiming

doi:10.1007/978-3-319-97304-3_72

Cited by 7 publications

(2 citation statements)

References 21 publications

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“…In the age of big data, machine learning is applied to many fields, such as road recognition [ 1 ] for autonomous driving, tumor recognition, [ 2 ] and default risk forecast, [ 3 ] etc. However, because of field characteristics, datasets from many domains are imbalanced.…”

Section: Introductionmentioning

confidence: 99%

Imbalanced Learning with Oversampling based on Classification Contribution Degree

Jiang

Yang

Liu

2021

Advcd Theory and Sims

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Imbalanced datasets exist commonly in the real world, which leads to poor performance of general machine learning models because of skewed class distribution. To address the data‐imbalance problem, a novel oversampling method based on classification contribution degree, called OS‐CCD is presented. First a new concept, classification contribution degree, is established based on micro and macro information extracted from raw datasets. With the classification contribution degree, OS‐CCD enables positive samples near the class boundary and located in an area with high density of positive samples to generate more synthetic samples than others. Furthermore, the neighbor selection for oversampling is no longer random but in the light of a selected probability. Experimental results on 12 benchmark datasets substantiate that four commonly used classifiers with the oversampling method outperform those with six popular oversampling methods in terms of accuracy, F1‐score and AUC.

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Section: Introductionmentioning

confidence: 99%

Imbalanced Learning with Oversampling based on Classification Contribution Degree

Jiang

Yang

Liu

2021

Advcd Theory and Sims

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“…FMs are applicable to any variables with real feature and are not restricted to recommender systems. FM gives a promising direction for the prediction purpose in regression, classification, and ranking [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market

Wang

Tang

2021

Complexity

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With a focus in the financial market, stock market dynamics forecasting has received much attention. Predicting stock market fluctuations is usually challenging due to the nonlinear and nonstationary time series of stock prices. The Elman recurrent network is renowned for its capability of dealing with dynamic information, which has made it a successful application to predicting. We developed a hybrid approach which combined Elman recurrent network with factorization machine (FM) technique, i.e., the FM-Elman neural network, to predict stock market volatility. In this paper, the Standard & Poor’s 500 Composite Stock Price (S&P 500) index, the Dow Jones industrial average (DJIA) index, the Shanghai Stock Exchange Composite (SSEC) index, and the Shenzhen Securities Component Index (SZI) were used to demonstrate the validity of our proposed FM-Elman model in time-series prediction. The results were compared with predictions obtained from the other two models which are basic BP neural network and the Elman neural network. Some experiments showed that the FM-Elman model outperforms others through different accuracy measures. Furthermore, the effects of volatility degree on prediction performance from different stock indexes were investigated. An interesting phenomenon had been found through some numerical experiments on the effects of different user-specified dimensions on the proposed FM-Elman neural network.

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Sparse Ordinal Regression via Factorization Machines

Liu

Zeng

2019

PRICAI 2019: Trends in Artificial Intelligence

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Robust Factorization Machines for Credit Default Prediction

Cited by 7 publications

References 21 publications

Imbalanced Learning with Oversampling based on Classification Contribution Degree

Imbalanced Learning with Oversampling based on Classification Contribution Degree

Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market

Sparse Ordinal Regression via Factorization Machines

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