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machine learning algorithm credit risk prediction model

Machine learning algorithm credit risk prediction model

引用

international conference on Applications and Techniques in Cyber Intelligence, ATCI 2020

作者： Wang, Liping An, Fanglin Anhui Audit College Hefei230001 China School of Computer Science and Cyberspace Security Hainan University Haikou570228 China

ISBN: (纸本)9783030539795

Due to the rapid development of China’s economy in recent years, banking business is booming. Credit business is one of the mainstream business of banks, but how to evaluate the credit risk of borrowers has become one of the hot topics in the Internet finance industry, which has attracted more and more attention. Since the launch of China’s credit system in 2013, the active risk control of credit business has been one of the hot topics in China’s financial field. Its essence is to divide customers into credit customers and non credit customers. With the rapid development of modern computer business, machine learning algorithm has gradually been popularized and applied in the financial field. Combined with gbdt algorithm in machine learning algorithm, the bank customer’s basic information, flow records, user detection information, user detection scale and other relevant data are used for comprehensive evaluation. Finally, an example is given to analyze the correlation. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： machine learning

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Network Intrusion Detection System Using Reinforcement learning Techniques

Network Intrusion Detection System Using Reinforcement Learn...

引用

international conference on Circuit, Power and computing Technologies (ICCPCT)

作者： Malika Malik Kamaljit Singh Saini CSE Chandigarh University Kharar India

Developing network intrusion detection systems (IDS) presents significant challenges due to the evolving nature of threats and the diverse range of network applications. Existing IDSs often struggle to detect dynamic attack patterns and covert attacks, leading to misidentified network vulnerabilities and degraded system performance. These requirements must be met via dependable, scalable, effective, and adaptable IDS designs. Our IDS can recognise and classify complex network threats by combining the Deep Q-Network (DQN) algorithm with distributed agents and attention techniques.. Our proposed distributed multi-agent IDS architecture has many advantages for guiding an all-encompassing security approach, including scalability, fault tolerance, and multi-view analysis. We conducted experiments using industry-standard datasets including NSL-KDD and CICIDS2017 to determine how well our model performed. The results show that our IDS outperforms others in terms of accuracy, precision, recall, F1-score, and false-positive rate. Additionally, we evaluated our model's resistance to black-box adversarial attacks, which are commonly used to take advantage of flaws in machine learning. Under these difficult circumstances, our model performed quite *** used a denoising autoencoder (DAE) for further model strengthening to improve the IDS's robustness. Lastly, we evaluated the effectiveness of our zero-day defenses, which are designed to mitigate attacks exploiting unknown vulnerabilities. Through our research, we have developed an advanced IDS solution that addresses the limitations of traditional approaches. Our model demonstrates superior performance, robustness against adversarial attacks, and effective zero-day defenses. By combining deep reinforcement learning, distributed agents, attention techniques, and other enhancements, we provide a reliable and comprehensive solution for network security.

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learning Bezier-Durrmeyer Type Descriptors for Classifying Curves – Preliminary Studies 1

引用

22nd international conference on Artificial Intelligence and soft computing, ICAISC 2023

作者： Krzyżak, Adam Rafajlowicz, Wojciech Rafajlowicz, Ewaryst Department of Computer Science Concordia University 1455 De Maisonneuve West MontrealQCH3G 1M8 Canada Szczecin Poland Faculty of Information and Comunication Technology Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (数字)9783031425059

ISBN: (纸本)9783031425042

We aim to propose a new approach for generating descriptors for functional data that are represented by curves that may not be functions but rather parametrically described curves in 2D, 3D, etc. The idea of generating these descriptors is based on the Bezier curves, but instead of using classical control points, we propose to generalize the Durrmeyer approach for parametrically defined vector curves. The Durrmeyer-type descriptors are then estimated from noisy samples of the underlying curve and serve as input vectors to a selected classifier that is learned to recognize from which class of curves noisy observations come. The Bezier curves are not rapidly convergent. However, our aim is not to reconstruct functions but to recognize them, maintaining the shape-preserving and variation-diminishing properties of these curves that increase the classification accuracy in the presence of noise without invoking pre-filtering procedures. For more complicated curves, one can directly apply our approach to their parts in a similar way as polynomial splines are used. The proposed algorithm for learning descriptors of the Bezier-Durrmeyer type and training classifiers on them was tested on synthetic, but interesting per se, data from two families of the Lissajous curves observed with high amplitude noise. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Polynomial approximation

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The Extreme Value Evolving Predictor in Multiple Time Series learning 20th

The Extreme Value Evolving Predictor in Multiple Time Series...

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20th international conference on Artificial Intelligence and soft computing (ICAISC)

作者： Ayres, Amanda O. C. Von Zuben, Fernando J. Univ Estadual Campinas Sch Elect & Comp Engn Campinas Brazil

ISBN: (纸本)9783030879860;9783030879853

This paper extends the evolving fuzzy-rule-based algorithm denoted Extreme Value evolving Predictor (EVeP) to deal with multivariate time series. EVeP offers a statistically well-founded approach to the online definition of the fuzzy granules at the antecedent and consequent parts of evolving fuzzy rules. The interplay established by these granules is used to formulate a regularized multitask learning problem which employs a sparse graph of the structural relationship promoted by the rules. With this multitask strategy, the Takagi-Sugeno consequent terms of the rules are then properly determined. In this extended version, called Extreme Value evolving Predictor in Multiple Time Series learning (EVeP MTSL), we propose an approach that resorts to the similarity degree among the time series. The similarity is calculated by the distance correlation statistical measure extracted from a sliding window of data points belonging to the multiple time series. Noticing that each fuzzy rule is part of a specific time series predictor, the new unified model called EVeP MTSL updates the sparse graph by composing the relationship established by each pair of fuzzy rules (already provided by EVeP) with the similarity degree of their corresponding time series. We are then exploring not only the current interplay of the multiple rules that compose each evolving predictor, but also the current correlation of the multiple time series being simultaneously predicted. Two computational experiments reveal the superior performance of EVeP MTSL when compared with other contenders devoted to online multivariate time series prediction.

关键词： Evolving fuzzy-rule-based systems Online learning Extreme Value Theory Multitask learning Multivariate time series prediction

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learning Activation Functions for Adversarial Attack Resilience in CNNs 1

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22nd international conference on Artificial Intelligence and soft computing, ICAISC 2023

作者： Salimi, Maghsood Loni, Mohammad Sirjani, Marjan Malardalen University Vasteras Sweden

ISBN: (数字)9783031425059

ISBN: (纸本)9783031425042

Adversarial attacks on convolutional neural networks (CNNs) have been a serious concern in recent years, as they can cause CNNs to produce inaccurate predictions. Through our analysis of training CNNs with adversarial examples, we discovered that this was primarily caused by naïvely selecting ReLU as the default choice for activation functions. In contrast to the focus of recent works on proposing adversarial training methods, we study the feasibility of an innovative alternative: learning novel activation functions to make CNNs more resilient to adversarial attacks. In this paper, we propose a search framework that combines simulated annealing and late acceptance hill-climbing to find activation functions that are more robust against adversarial attacks in CNN architectures. The proposed search method has superior search convergence compared to commonly used baselines. The proposed method improves the resilience to adversarial attacks by achieving up to 17.1%, 22.8%, and 16.6% higher accuracy against BIM, FGSM, and PGD attacks, respectively, over ResNet-18 trained on the CIFAR-10 dataset. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Simulated annealing

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A Preliminary Study on Deep Transfer learning Applied to Image Classification for Small Datasets 15th

A Preliminary Study on Deep Transfer Learning Applied to Ima...

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15th international conference on soft computing Models in Industrial and Environmental Applications, SOCO 2020

作者： Molina, Miguel Ángel Asencio-Cortés, Gualberto Riquelme, José C. Martínez-Álvarez, Francisco Data Science & Big Data Lab Pablo de Olavide University Seville41013 Spain Department of Computer Science University of Seville Seville Spain

ISBN: (纸本)9783030578015

A new transfer learning strategy is proposed for image classification in this work, based on an 8-layer convolutional neural network. The transfer learning process consists in a training phase of the neural network on a source dataset of images. Then, the last two layers are retrained using a different small target dataset of images. A preliminary study was conducted to train and test the transfer learning proposal on Malaria cell images for a binary classification problem. The methodology proposed has provided a of improvement with respect to other three different strategies of training non-transfer learning models. The results achieved are quite promising and encourage to conduct further research in this field. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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Comparison of Feature Transformation and Missing Value Imputation Methods for Nutrient-Based Food Group Classification

Comparison of Feature Transformation and Missing Value Imput...

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international conference on Informatics and computing (ICIC)

作者： Fatma Indriani Dwi Kartini Dodon Turianto Nugrahadi Triando Hamonangan Saragih Department of Computer Science Lambung Mangkurant University Banjarbaru Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Accurate classification of food items based on nutrient profiles is crucial for dietary analysis, nutritional research, and public health. This study investigates the effectiveness of various imputation and feature transformation methods in improving the performance of nutrient-based food group classification, particularly in the presence of skewed data and missing values. The dataset, sourced from the Tabel Komposisi Pangan Indonesia (TKPI) 2017, comprises 1,145 food items across 12 food groups, characterized by 20 nutrient features. We compared mean, median, K-nearest neighbors (KNN), iterative imputation, and iterative imputation with RandomForestRegressor for handling missing values, combined with feature transformations including standard scaling, log transformation, Box-Cox, and Yeo-Johnson. LightGBM with SMOTE was employed as the classification model, evaluated using macro-average accuracy to ensure equal importance to all classes. Our results show that median imputation paired with either Yeo-Johnson or Box-Cox transformations achieved the highest macro-average accuracy (0.8009), improving upon the baseline model (mean imputation with no transformation, accuracy 0.7721). The study revealed challenges in classifying certain food groups, particularly spices, highlighting limitations of nutrient-based classification for some categories. These findings suggest that appropriate imputation and transformation methods can improve classification accuracy in nutrient-based food group classification, while also indicating areas for future research to address persistent challenges.

关键词： Accuracy machine learning Nearest neighbor methods Nutrients Imputation Iterative methods Reliability Public healthcare Informatics Standards

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An Intrusion Detection Model for CICIDS-2017 Dataset Using machine learning Algorithms

An Intrusion Detection Model for CICIDS-2017 Dataset Using M...

引用

Advances in computing, Communication & Materials (ICACCM), 2020 international conference on

作者： Shailesh Singh Panwar Y. P. Raiwani Lokesh Singh Panwar Dept. of Computer Science and Engg. H.N.B. Garhwal University Srinagar Garhwal Uttarakhand India Dept. of Electronics and Comm. Engg. Graphic Era University Dehradun Uttarakhand India

ISBN: (纸本)9781665474405

Due to excessive use of the internet, keeping the network secure and transferring data securely across networks has become a difficult task. To recognize distinct types of network (internet) attacks, need machine learning strategies, to develop various types of the intrusion detection system. The main reason behind using different types of intrusion detection strategies is that the attackers constantly receiving information about the network traffic. Therefore, the intrusion detection system is used to easily identify the attacks and to thwart those attacks. In this paper, we have used the CICIDS-2017 dataset, which is a labeled dataset, for analyzing the result of eight different supervised classification techniques (GaussianNB (GNB), BernoulliNB (BNB), Decision Tree, KNN, Logistic Regression, SVM, Random Forest and SGD). The dataset is divided into five different days; each has different types of class attacks (DoS, PortScan, Botnet, Web Attacks, Infiltration, Heartbleed and DDoS). We have proposed a model with three stages. The first stage data-preprocessing, which includes several steps such as extracting independent and dependent variable, splitting dataset, finding missing value, feature scaling on the dataset and encoding categorical data, then in feature selection stage, finding out unique features using RFE (Recursive feature elimination) technique. Finally, test the result of eight different supervised classification techniques using cross-validation on the CICIDS-2017 dataset.

关键词： Support vector machines machine learning algorithms Intrusion detection Gaussian processes Telecommunication traffic Feature extraction Encoding

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Simulation of Chlorophyll-a Concentration in Donghu Lake Based on GA-ELM and Multiple Water Quality Indexes

Simulation of Chlorophyll-a Concentration in Donghu Lake Bas...

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international conference on Algorithms, High Performance computing, and Artificial Intelligence (AHPCAI)

作者： Tang, Xiaodong Huang, Mutao Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China

ISBN: (数字)9781510651890

ISBN: (纸本)9781510651890;9781510651883

Simulation of chlorophyll-a concentration is an important method for monitoring lake eutrophication. In recent years, plenty of achievements have made in the study on chlorophyll-a concentration inversion model and water quality numerical model. However, the application of these two kinds of models is limited in lakes water with small data sample. In this paper, building non-mechanism model based on the relationship between chlorophyll-a concentration and multiple water quality indexes is proved to be feasible, at the same time, linear regression (LR) model, extreme learning machine (ELM) model, and ELM optimized by genetic algorithm (GA-ELM) model were built to simulate chlorophyll-a concentration. The simulation results show that the simulation effect of ELM model is better than that of LR model, and GA-ELM model works best. In addition, genetic algorithm (GA) effectively improves the simulation effect of ELM, R2 of GA-ELM model increased by 0.1570, RMSE decreased by 7.12 mu g/L, MAPE decreased by 0.7117 when compared with ELM model. The study provides a simple and effective method to simulate chlorophyll-a concentration, which can be applied for practical monitoring eutrophication in Donghu Lake.

关键词： Chlorophyll-a concentration Modeling and simulation Donghu Lake Extreme learning machine Genetic algorithm

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Distributed image fire detection and alarm system based on machine learning

Distributed image fire detection and alarm system based on m...

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international conference on Application of Intelligent Systems in Multi-modal Information Analytics, MMIA 2020

作者： Du, Yang Fan, Yufeng Emergency Management Department Shenyang Fire Research Institute Shenyang China

ISBN: (纸本)9783030515553

With the continuous acceleration of the construction of China’s "smart city", the social unit video surveillance system has been rapidly popularized. Based on the current development of image fire detection, this paper analyzes the problems of limiting the development of image fire detection system, in order to expand the functions and applications of current video surveillance systems, this paper applies artificial intelligence machine learning methods, establishes a framework, data flow and training model of distributed fire image detection and alarm system based on machine theory, and realizes the functional value-added and application expansion of the existing video surveillance system of the social unit. © Springer Nature Switzerland AG 2021.

关键词： Alarm systems

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