ARTIFICIAL METAPLASTICITY NEURAL NETWORK APPLIED TO CREDIT SCORING

2011 ◽  
Vol 21 (04) ◽  
pp. 311-317 ◽  
Author(s):  
ALEXIS MARCANO-CEDEÑO ◽  
A. MARIN-DE-LA-BARCENA ◽  
J. JIMENEZ-TRILLO ◽  
J. A. PIÑUELA ◽  
D. ANDINA
Keyword(s):  
Neural Network ◽  
Financial Institutions ◽  
State Of The Art ◽  
Credit Scoring ◽  
Error Rates ◽  
Classification Algorithms ◽  
Data Sets ◽  
Training Algorithm ◽  
Low Probability ◽  
German Data

The assessment of the risk of default on credit is important for financial institutions. Different Artificial Neural Networks (ANN) have been suggested to tackle the credit scoring problem, however, the obtained error rates are often high. In the search for the best ANN algorithm for credit scoring, this paper contributes with the application of an ANN Training Algorithm inspired by the neurons' biological property of metaplasticity. This algorithm is especially efficient when few patterns of a class are available, or when information inherent to low probability events is crucial for a successful application, as weight updating is overemphasized in the less frequent activations than in the more frequent ones. Two well-known and readily available such as: Australia and German data sets has been used to test the algorithm. The results obtained by AMMLP shown have been superior to state-of-the-art classification algorithms in credit scoring.

Online Learning Based on Online DCA and Application to Online Classification

2020 ◽  
Vol 32 (4) ◽  
pp. 759-793 ◽  
Author(s):  
Hoai An Le Thi ◽  
Vinh Thanh Ho
Keyword(s):  
Online Learning ◽  
State Of The Art ◽  
Classification Algorithms ◽  
Data Sets ◽  
Prediction Problem ◽  
Dc Algorithm ◽  
Difference Of Convex Functions ◽  
Online Classification ◽  
Learning Techniques ◽  
Difference Of Convex

We investigate an approach based on DC (Difference of Convex functions) programming and DCA (DC Algorithm) for online learning techniques. The prediction problem of an online learner can be formulated as a DC program for which online DCA is applied. We propose the two so-called complete/approximate versions of online DCA scheme and prove their logarithmic/sublinear regrets. Six online DCA-based algorithms are developed for online binary linear classification. Numerical experiments on a variety of benchmark classification data sets show the efficiency of our proposed algorithms in comparison with the state-of-the-art online classification algorithms.

scholarly journals An implementation of ensemble methods, logistic regression, and neural network for default prediction in Peer-to-Peer lending

2021 ◽  
Vol 39 (1) ◽  
pp. 163-197
Author(s):  
Aneta Dzik-Walczak ◽  
Mateusz Heba
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Logistic Regression ◽  
Regression Model ◽  
Financial Institutions ◽  
Logistic Regression Model ◽  
Credit Scoring ◽  
Peer To Peer ◽  
Ensemble Model ◽  
Peer Lending

Credit scoring has become an important issue because competition among financial institutions is intense and even a small improvement in predictive accuracy can result in significant savings. Financial institutions are looking for optimal strategies using credit scoring models. Therefore, credit scoring tools are extensively studied. As a result, various parametric statistical methods, non-parametric statistical tools and soft computing approaches have been developed to improve the accuracy of credit scoring models. In this paper, different approaches are used to classify customers into those who repay the loan and those who default on a loan. The purpose of this study is to investigate the performance of two credit scoring techniques, the logistic regression model estimated on categorized variables modified with the use of WOE (Weight of Evidence) transformation, and neural networks. We also combine multiple classifiers and test whether ensemble learning has better performance. To evaluate the feasibility and effectiveness of these methods, the analysis is performed on Lending Club data. In addition, we investigate Peer-to-peer lending, also called social lending. From the results, it can be concluded that the logistic regression model can provide better performance than neural networks. The proposed ensemble model (a combination of logistic regression and neural network by averaging the probabilities obtained from both models) has higher AUC, Gini coefficient and Kolmogorov-Smirnov statistics compared to other models. Therefore, we can conclude that the ensemble model allows to successfully reduce the potential risks of losses due to misclassification costs.

scholarly journals Semi-supervised multi-label classification using an extended graph-based manifold regularization

2022 ◽  
Author(s):  
Ding Li ◽  
Scott Dick
Keyword(s):  
Supervised Learning ◽  
State Of The Art ◽  
Ground Truth ◽  
Classification Algorithms ◽  
Data Sets ◽  
Manifold Regularization ◽  
Regularization Algorithm ◽  
Weighting Strategy ◽  
Extended Graph ◽  
General Vector

AbstractGraph-based algorithms are known to be effective approaches to semi-supervised learning. However, there has been relatively little work on extending these algorithms to the multi-label classification case. We derive an extension of the Manifold Regularization algorithm to multi-label classification, which is significantly simpler than the general Vector Manifold Regularization approach. We then augment our algorithm with a weighting strategy to allow differential influence on a model between instances having ground-truth vs. induced labels. Experiments on four benchmark multi-label data sets show that the resulting algorithm performs better overall compared to the existing semi-supervised multi-label classification algorithms at various levels of label sparsity. Comparisons with state-of-the-art supervised multi-label approaches (which of course are fully labeled) also show that our algorithm outperforms all of them even with a substantial number of unlabeled examples.

scholarly journals Grapheme-to-Phoneme Conversion with Convolutional Neural Networks

2019 ◽  
Vol 9 (6) ◽  
pp. 1143 ◽  
Author(s):  
Sevinj Yolchuyeva ◽  
Géza Németh ◽  
Bálint Gyires-Tóth
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Language Processing ◽  
Speech Synthesis ◽  
State Of The Art ◽  
Error Rates ◽  
Written Form ◽  
G2p Conversion

Grapheme-to-phoneme (G2P) conversion is the process of generating pronunciation for words based on their written form. It has a highly essential role for natural language processing, text-to-speech synthesis and automatic speech recognition systems. In this paper, we investigate convolutional neural networks (CNN) for G2P conversion. We propose a novel CNN-based sequence-to-sequence (seq2seq) architecture for G2P conversion. Our approach includes an end-to-end CNN G2P conversion with residual connections and, furthermore, a model that utilizes a convolutional neural network (with and without residual connections) as encoder and Bi-LSTM as a decoder. We compare our approach with state-of-the-art methods, including Encoder-Decoder LSTM and Encoder-Decoder Bi-LSTM. Training and inference times, phoneme and word error rates were evaluated on the public CMUDict dataset for US English, and the best performing convolutional neural network-based architecture was also evaluated on the NetTalk dataset. Our method approaches the accuracy of previous state-of-the-art results in terms of phoneme error rate.

Benchmarking state-of-the-art classification algorithms for credit scoring

2003 ◽  
Vol 54 (6) ◽  
pp. 627-635 ◽  
Author(s):  
B Baesens ◽  
T Van Gestel ◽  
S Viaene ◽  
M Stepanova ◽  
J Suykens ◽  
...  
Keyword(s):  
State Of The Art ◽  
Credit Scoring ◽  
Classification Algorithms

scholarly journals K-margin-based Residual-Convolution-Recurrent Neural Network for Atrial Fibrillation Detection

2019 ◽  
Author(s):  
Yuxi Zhou ◽  
Shenda Hong ◽  
Junyuan Shang ◽  
Meng Wu ◽  
Qingyun Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Recurrent Neural Network ◽  
State Of The Art ◽  
Class Imbalance ◽  
Heart Rhythm ◽  
Error Rates ◽  
Detection Task ◽  
Training Data

Atrial Fibrillation (AF) is an abnormal heart rhythm which can trigger cardiac arrest and sudden death. Nevertheless, its interpretation is mostly done by medical experts due to high error rates of computerized interpretation. One study found that only about 66% of AF were correctly recognized from noisy ECGs. This is in part due to insufficient training data, class skewness, as well as semantical ambiguities caused by noisy segments in an ECG record. In this paper, we propose a K-margin-based Residual-Convolution-Recurrent neural network (K-margin-based RCR-net) for AF detection from noisy ECGs. In detail, a skewness-driven dynamic augmentation method is employed to handle the problems of data inadequacy and class imbalance. A novel RCR-net is proposed to automatically extract both long-term rhythm-level and local heartbeat-level characters. Finally, we present a K-margin-based diagnosis model to automatically focus on the most important parts of an ECG record and handle noise by naturally exploiting expected consistency among the segments associated for each record. The experimental results demonstrate that the proposed method with 0.8125 F1NAOP score outperforms all state-of-the-art deep learning methods for AF detection task by 6.8%.

scholarly journals PWNet: An Adaptive Weight Network for the Fusion of Panchromatic and Multispectral Images

2020 ◽  
Vol 12 (17) ◽  
pp. 2804
Author(s):  
Junmin Liu ◽  
Yunqiao Feng ◽  
Changsheng Zhou ◽  
Chunxia Zhang
Keyword(s):  
Neural Network ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
State Of The Art ◽  
Spectral Information ◽  
Data Sets ◽  
Multispectral Images ◽  
Image Prior ◽  
Adaptive Weight ◽  
Component Substitution

Pansharpening is a typical image fusion problem, which aims to produce a high resolution multispectral (HRMS) image by integrating a high spatial resolution panchromatic (PAN) image with a low spatial resolution multispectral (MS) image. Prior arts have used either component substitution (CS)-based methods or multiresolution analysis (MRA)-based methods for this propose. Although they are simple and easy to implement, they usually suffer from spatial or spectral distortions and could not fully exploit the spatial and/or spectral information existed in PAN and MS images. By considering their complementary performances and with the goal of combining their advantages, we propose a pansharpening weight network (PWNet) to adaptively average the fusion results obtained by different methods. The proposed PWNet works by learning adaptive weight maps for different CS-based and MRA-based methods through an end-to-end trainable neural network (NN). As a result, the proposed PWN inherits the data adaptability or flexibility of NN, while maintaining the advantages of traditional methods. Extensive experiments on data sets acquired by three different kinds of satellites demonstrate the superiority of the proposed PWNet and its competitiveness with the state-of-the-art methods.

scholarly journals Accelerating and Improving AlphaZero Using Population Based Training

2020 ◽  
Vol 34 (01) ◽  
pp. 1046-1053
Author(s):  
Ti-Rong Wu ◽  
Ting-Han Wei ◽  
I-Chen Wu
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Population Based ◽  
Training Algorithm ◽  
Time Cost ◽  
Huge Amount ◽  
Additional Time ◽  
Training Time ◽  
Source State ◽  
Training Cost

AlphaZero has been very successful in many games. Unfortunately, it still consumes a huge amount of computing resources, the majority of which is spent in self-play. Hyperparameter tuning exacerbates the training cost since each hyperparameter configuration requires its own time to train one run, during which it will generate its own self-play records. As a result, multiple runs are usually needed for different hyperparameter configurations. This paper proposes using population based training (PBT) to help tune hyperparameters dynamically and improve strength during training time. Another significant advantage is that this method requires a single run only, while incurring a small additional time cost, since the time for generating self-play records remains unchanged though the time for optimization is increased following the AlphaZero training algorithm. In our experiments for 9x9 Go, the PBT method is able to achieve a higher win rate for 9x9 Go than the baselines, each with its own hyperparameter configuration and trained individually. For 19x19 Go, with PBT, we are able to obtain improvements in playing strength. Specifically, the PBT agent can obtain up to 74% win rate against ELF OpenGo, an open-source state-of-the-art AlphaZero program using a neural network of a comparable capacity. This is compared to a saturated non-PBT agent, which achieves a win rate of 47% against ELF OpenGo under the same circumstances.

Modeling credit scoring using neural network ensembles

Kybernetes ◽  
2014 ◽  
Vol 43 (7) ◽  
pp. 1114-1123 ◽  
Author(s):  
Chih-Fong Tsai ◽  
Chihli Hung
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Credit Scoring ◽  
Data Sets ◽  
Content Type ◽  
Learning Techniques ◽  
Neural Network Ensembles ◽  
Hybrid Neural Networks ◽  
Network Ensembles

Purpose – Credit scoring is important for financial institutions in order to accurately predict the likelihood of business failure. Related studies have shown that machine learning techniques, such as neural networks, outperform many statistical approaches to solving this type of problem, and advanced machine learning techniques, such as classifier ensembles and hybrid classifiers, provide better prediction performance than single machine learning based classification techniques. However, it is not known which type of advanced classification technique performs better in terms of financial distress prediction. The paper aims to discuss these issues. Design/methodology/approach – This paper compares neural network ensembles and hybrid neural networks over three benchmarking credit scoring related data sets, which are Australian, German, and Japanese data sets. Findings – The experimental results show that hybrid neural networks and neural network ensembles outperform the single neural network. Although hybrid neural networks perform slightly better than neural network ensembles in terms of predication accuracy and errors with two of the data sets, there is no significant difference between the two types of prediction models. Originality/value – The originality of this paper is in comparing two types of advanced classification techniques, i.e. hybrid and ensemble learning techniques, in terms of financial distress prediction.

scholarly journals Machine Translation between paired Single Cell Multi Omics Data

2021 ◽  
Author(s):  
Xabier Martinez-de-Morentin ◽  
Sumeer A. Khan ◽  
Robert Lehmann ◽  
Jesper Tegner ◽  
David Gomez-Cabrero
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
State Of The Art ◽  
Data Sets ◽  
Omics Data ◽  
Paired Data ◽  
Omics Technologies ◽  
Cell Numbers ◽  
Latent Space ◽  
Single Data

AbstractSingle-cell multi-omics technologies enable profiling of several data-modalities from the same cell. We designed LIBRA, a Neural Network based framework, for learning translations between paired multi-omics profiles into a shared latent space. We demonstrate LIBRA to be state-of-the-art for multi-omics clustering. In addition, LIBRA is more robust with decreasing cell-numbers compared with existing tools. Training LIBRA on paired data-sets, LIBRA predicts multi-omic profiles using only a single data-modality from the same biological system.

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