Deep Convolutional Neural Networks Versus Multilayer Perceptron for Financial Prediction

Neagoe, Victor-Emil; Ciotec, Adrian-Dumitru; Cucu, George-Sorin

doi:10.1109/iccomm.2018.8484751

Cited by 42 publications

(21 citation statements)

References 6 publications

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“…In Zhu et al [10], deep learning was utilized for the 1 st time by applying convolutional neural networks (CNN) approach through the transformation of features to gray scale images, their R-CNN model improved on the area under curve (AUC) of RF and logistic regression (LR) by around 10%. A thorough analysis of different neural networks, such as Multilayer Perceptron and CNNs for credit defaulting can be found in Neagoe et al [11].…”

Section: Related Workmentioning

confidence: 99%

Comparison of Different Ensemble Methods in Credit Card Default Prediction

2021

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Credit card defaults pause a business-critical threat in banking systems thus prompt detection of defaulters is a crucial and challenging research problem. Machine learning algorithms must deal with a heavily skewed dataset since the ratio of defaulters to non-defaulters is very small. The purpose of this research is to apply different ensemble methods and compare their performance in detecting the probability of defaults customer’s credit card default payments in Taiwan from the UCI Machine learning repository. This is done on both the original skewed dataset and then on balanced dataset several studies have showed the superiority of neural networks as compared to traditional machine learning algorithms, the results of our study show that ensemble methods consistently outperform Neural Networks and other machine learning algorithms in terms of F1 score and area under receiver operating characteristic curve regardless of balancing the dataset or ignoring the imbalance

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Section: Related Workmentioning

confidence: 99%

Comparison of Different Ensemble Methods in Credit Card Default Prediction

2021

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“…Usually, a gradient-descent algorithm called back-propagation is used to train an MLP. In this algorithm, a maximum error is defined to be used as a criterion to stop the iterative weight update process [14].…”

Section: A Single Modelsmentioning

confidence: 99%

Rapid COVID-19 Diagnosis Using Deep Learning of the Computerized Tomography Scans

Tabrizchi

Mosavi

Szabo-Gali

et al. 2020

Preprint

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Several studies suggest that COVID-19 may be accompanied by symptoms such as a dry cough, muscle aches, sore throat, and mild to moderate respiratory illness. The symptoms of this disease indicate the fact that COVID-19 causes noticeable negative effects on the lungs. Therefore, considering the health status of the lungs using X-rays and CT scans of the chest can significantly help diagnose COVID-19 infection. Due to the fact that most of the methods that have been proposed to COVID-19 diagnose deal with the lengthy testing time and also might give more false positive and false negative results, this paper aims to review and implement artificial intelligence (AI) image-based diagnosis methods in order to detect coronavirus infection with zero or near to zero false positives and false negatives rates. Besides the already existing AI image-based medical diagnosis method for the other well-known disease, this study aims on finding the most accurate COVID-19 detection method among AI methods such as machine learning (ML) and artificial neural network (ANN), ensemble learning (EL) methods.

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“…The remaining features are marked as OTHER. we have tested Multi-layer perceptron [MLP] which was used for financial forecasting [18], [19]. Additionally, we have performed a further comparison with a XGBoost methodology that was applied in a different domain within the financial scenario (i.e.…”

Section: Comparisons With Respect To ML and Dl Approachesmentioning

confidence: 99%

“…For what concerns the financial forecasting different ML and DL algorithms were proposed for learning in the presence of sequence data. These approaches range from standard Deep Neural Network (DNN) [18], [19] to recurrent neural network (i.e. Long-short-term memory) [20]- [22].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Capital Markets: Decision Support System for Outcome Analysis

Rosati

Romeo

Goday³

et al. 2020

IEEE Access

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Decision support systems using Artificial Intelligence in the context of financial services includes different application ranging from investment advice to financial trading. The analysis of order flow provides many challenges that can be addressed by Machine Learning (ML) techniques in order to determine an optimal dynamic trading strategy. The first step in this direction is represented by the outcome analysis of order flow: the model should identify strong predictors that determine a positive/negative outcome. The aim of this work is the proposal of a closed-loop ML approach based on decision tree (DT) model to perform outcome analysis on financial trading data. The overall approach is integrated in a Decision Support System for Outcome Analysis (DSS-OA). Taking into account the model complexity, the DT algorithm enables to generate explanations that allow the user to understand (i) how this outcome is reached (decision rules) and (ii) the most discriminative outcome predictors (feature importance). The closed-loop approach allows the users to interact directly with the proposed DSS-OA by retraining the algorithm with the goal to a finer-grained outcome analysis. The experimental results and comparisons demonstrated high-interpretability and predictive performance of the proposed DSS-OA by providing a valid and fast system for outcome analysis on financial trading data. Moreover, the Proof of Concept evaluation demonstrated the impact of the proposed DSS-OA in the outcome analysis scenario.

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Deep Convolutional Neural Networks Versus Multilayer Perceptron for Financial Prediction

Cited by 42 publications

References 6 publications

Comparison of Different Ensemble Methods in Credit Card Default Prediction

Comparison of Different Ensemble Methods in Credit Card Default Prediction

Rapid COVID-19 Diagnosis Using Deep Learning of the Computerized Tomography Scans

Machine Learning in Capital Markets: Decision Support System for Outcome Analysis

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