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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5771-5785页

作者： Li, Jinbo Liu, Peng Chen, Long Pedrycz, Witold Ding, Weiping Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China China Unicom Research Institute Beijing100048 China Henan University International Business School Zhengzhou47500 China University of Macau Department of Computer and Information Science 999078 China University of Alberta Department of Electrical and Computer Engineering EdmontonAB T6R 2V4 Canada City University of Macau Faculty of Data Science 999078 China

The integration of different learning paradigms has long been a focus of machine learning research, aimed at overcoming the inherent limitations of individual methods. Fuzzy rule-based models excel in interpretability and have seen widespread application across diverse fields. However, they face challenges such as complex design specifications and scalability issues with large datasets. The fusion of different techniques and strategies, particularly gradient boosting, with fuzzy rule-based models offers a robust solution to these challenges. This article proposes an integrated fusion framework that merges the strengths of both paradigms to enhance model performance and interpretability. At each iteration, a fuzzy rule-based model is constructed and controlled by a dynamic factor to optimize its contribution to the overall ensemble. This control factor serves multiple purposes: it prevents model dominance, encourages diversity, acts as a regularization parameter, and provides a mechanism for dynamic tuning based on model performance, thus mitigating the risk of overfitting. Additionally, the framework incorporates a sample-based correction mechanism that allows for adaptive adjustments based on feedback from a validation set. Experimental results substantiate the efficacy of the presented gradient-boosting framework for fuzzy rule-based models, demonstrating performance enhancement, especially in terms of mitigating overfitting and complexity typically associated with many rules. By leveraging an optimal factor to govern the contribution of each model, the framework improves performance, maintains interpretability, and simplifies the maintenance and update of the models. © 2024 IEEE.

关键词： Optimization

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Leveraging XGBoost and Feature Importance for NBA Salary Classification

Leveraging XGBoost and Feature Importance for NBA Salary Cla...

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Information Technology Research and Innovation (ICITRI), International Conference on

作者： Musyaffa Ayman Rafif Rayhan Prawira Daksa Gede Sanjaya Indrajaya Alexander A S Gunawan Karli Eka Setiawan Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350376210

ISBN: (纸本)9798350376227

Focusing on feature importance, this study utilized the XGBoostClassifier to predict NBA player salaries within categorized salary bands. Oversampling techniques ensured balanced class representation, while feature importance analysis identified Minutes Played, Age, Points Scored, Games Started, and Turnovers as key factors influencing model predictions. The XGBoostClassifier achieved a balanced accuracy of 83% across all salary tiers, demonstrating its ability to navigate NBA player valuation complexities. Focusing on these key features further boosted average accuracy to 85%, highlighting the potential for optimization through prioritizing impactful attributes. Our findings suggest valuable avenues for future research, including leveraging feature importance for model refinement, expanding the dataset size, incorporating more relevant features, and investigating outliers for deeper insights into the multifaceted dynamics of NBA player salary classification.

关键词： Analytical models Technological innovation Accuracy Navigation Focusing Games Predictive models Remuneration Optimization Cost accounting

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Enhancing Machine Failure Detection with Ensemble Learning and Oversampling Techniques

Enhancing Machine Failure Detection with Ensemble Learning a...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Wilson Gregory Pribadi Maria Linneke Adjie Karli Eka Setiawan Computer Science Department School of Computer Science Data Science Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

In modern manufacturing, predictive maintenance plays a vital role in minimizing unexpected machine failures, thereby reducing downtime and maintenance expenses. This study explores the implementation of ensemble learning models, including Bagging, AdaBoost, Gradient Boosting, Random Forest, and XGBoost, to predict machine failures based on operational characteristics such as air temperature, process temperature, rotational speed, torque, and tool wear. To address the class imbalance commonly present in failure datasets, oversampling techniques like SMOTE (Synthetic Minority Over-sampling Technique) and ADASYN (Adaptive Synthetic Sampling) were used to improve the model’s performance. However, a couple of experiments shows that ensemble models without oversampling is better than those that uses these techniques. The Gradient Boosting model using just the oversampling performed best with an F1-score of 0.78 for class 1 and overall accuracy of 0.99 while specifically, the version without any oversampling made it to an F1 Score of even as high as 0. XGBoost and Bagging were the next best models with F1-scores of 0.76 and 0.75, respectively since then. In contrast, the F1-score of the model proposed in this study and the models using any kind of oversampling technique were lower than 0.70 of failure class. This is proof of the power of ensemble methods (especially Gradient Boosting), that as result can be deployed to forecast machine failures and encourage right preventive maintenance actions ensuring less interruptions and smoother operations.

关键词： Adaptation models Accuracy Temperature Torque Atmospheric modeling Velocity control Predictive models Boosting Ensemble learning Bagging

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HIV Inhibitory Activity Classification with Morgan Fingerprint and Integrated Graph-Based Deep Learning

HIV Inhibitory Activity Classification with Morgan Fingerpri...

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International Conference of Information and Communication Technology (ICoICT)

作者： Jocelyn Verna Siswanto Caroline Angelina Sunarya Agnes Calista Alexander Agung Santoso Gunawan Karli Eka Setiawan Computer Science Department Data Science Program School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350363432

ISBN: (纸本)9798350363449

This research offers a new perspective on predicting the activity of the HIV virus from the Drug Therapeutics program (DTP) Antiviral Screen by using the molecular data represented in SMILES notation. The topic has significance as it focuses on a major global health issue using modern computational approaches and has the potential to uncover new antiviral drug candidates, which could eventually save lives and improve public health outcomes. The study addresses the data imbalance between two classes, active and inactive, and employs the Morgan Fingerprint method for feature extraction, along with the Graph Convolutional Network (GCN) and Graph Attention Network (GAT) as the baseline architectures and the fusion of GCN's and GAT's main features as the proposed architecture. The random oversampling technique is applied to alleviate dataset imbalances. However, even though it improved the training process, the performance of the model flopped when the test set was fed into the model. Combining the main features in GCN and GAT, the proposed model was able to do the classification task more accurately. The attention mechanism from GAT allows the model to focus more on the parts that are more relevant and ignore the irrelevant ones. It managed to outperform the baseline models. Despite a high overall accuracy of 94%, the fusion model exhibits significant disparities in precision, recall, and f1-score metrics, potentially due to class imbalance. Random oversampling led to improved training but compromised model performance on the test set.

关键词： Training Measurement Drugs Attention mechanisms Computational modeling computer architecture Fingerprint recognition Predictive models Feature extraction data models

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Preventing the Popular Item Embedding Based Attack in Federated Recommendations 40

Preventing the Popular Item Embedding Based Attack in Federa...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Zhang, Jun Li, Huan Rong, Dazhong Zhao, Yan Chen, Ke Shou, Lidan Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Aalborg University Department of Computer Science Aalborg Denmark

ISBN: (纸本)9798350317152

Privacy concerns have led to the rise of federated recommender systems (FRS), which can create personalized models across distributed clients. However, FRS is vulnerable to poisoning attacks, where malicious users manipulate gradients to promote their target items intentionally. Existing attacks against FRS have limitations, as they depend on specific models and prior knowledge, restricting their real-world applicability. In our exploration of practical FRS vulnerabilities, we devise a model-agnostic and prior-knowledge-free attack, named PIECK (Popular Item Embedding based Attack). The core module of PIECK is popular item mining, which leverages embedding changes during FRS training to effectively identify the popular items. Built upon the core module, PIECK branches into two diverse solutions: The PIECKIPE solution employs an item popularity enhancement module, which aligns the embeddings of targeted items with the mined popular items to increase item exposure. The PIECKUEA further enhances the robustness of the attack by using a user embedding approximation module, which approximates private user embeddings using mined popular items. Upon identifying PIECK, we evaluate existing federated defense methods and find them ineffective against PIECK, as poisonous gradients inevitably overwhelm the cold target items. We then propose a novel defense method by introducing two regularization terms during user training, which constrain item popularity enhancement and user embedding approximation while preserving FRS performance. We evaluate PIECK and its defense across two base models, three real datasets, four top-tier attacks, and six general defense methods, affirming the efficacy of both PIECK and its defense. © 2024 IEEE.

关键词： Embeddings

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Enhancing Online Learning Experience: An Ensemble Approach for Classifying Student Adaptivity Level

Enhancing Online Learning Experience: An Ensemble Approach f...

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science and Information Technology in Smart Administration (ICSINTESA), International Conference of

作者： Belinda Mutiara Florencia Gabrielle Felicia Ariyanto Alexander Agung Santoso Gunawan Karli Eka Setiawan Computer Science Department School of Computer Science Data Science Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

The study investigates the increasing demand of online learning as a means of addressing education issues in the context of the COVID-19 epidemic. Online learning requires several adaptations for teaching methods, learning methodologies, and devices needs. The flexibility of both teachers and students is essential to these adaptations. This research uses dataset collected from a survey about student adaptability level in online education. Carrying out the preprocessing is a challenge as the data used in this research has imbalanced value on the target category. Based on this problem, the aim of this research was created, namely to categorize students’ adaptivity levels in online learning and also focuses on finding out approaches that can provide solutions to overcome imbalanced values in the dataset. The model uses ensemble methods - Bagging, Boosting, and Voting with machine learning algorithms. There are two models that stand out, with Soft Voting obtaining the best performance with $\mathbf{9 0 \%}$ accuracy.

关键词： Surveys Adaptation models Accuracy Machine learning algorithms Education Neural networks Boosting Ensemble learning Bagging Random forests

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A Multiclass Semi-Supervised Deep Convolutional Generative Adversarial Network for Music Genre Classification Using Mel-Frequency Cepstral Coefficients 2

A Multiclass Semi-Supervised Deep Convolutional Generative A...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Satapathy, Ashutosh Duda, Neelima MacHcha, Jaswanthi Thottempudi, Kokila Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science and Engineering Vijayawada India Velagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798350306415

The growing consumer base and expanding market for various music styles highlight the necessity of classifying music genres to cater to people's preferences. Manual music ranking is a labor-intensive process for listeners, prompting the need for a more efficient approach. This involves the extraction of Mel-frequency cepstral coefficient feature maps from the log Mel-spectrograms of audio clips. The extracted feature maps are supplied to a multiclass semi-supervised deep convolutional generative adversarial network where the discriminator behaves as a classifier. The training of models involves utilizing the GTZAN standardized dataset, a publicly accessible collection of thousands of audio files spanning ten different genres, from which 80% and 20% of the data are used for training and testing, respectively. Finally, the paper discusses the performance of the semi-supervised deep convolutional generative adversarial network through the RMSprop and Adam optimizers on the original and augmented labeled and unlabeled MFCC feature maps. Without any data augmentation, the discriminator achieves a training accuracy of 97.9% and a test accuracy of about 45.67%. In contrast, the discriminator's training accuracy is about 98.3%, and the test accuracy is 84.75% with data augmentation. © 2024 IEEE.

关键词： data Augmentation GTZAN Indian Music Genre Mel-frequency Cepstral Coefficients Multiclass Classification Semi-Supervised Deep Convolutional Generative Adversarial Network

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Breast Cancer Image Classification Obtained Through Dynamic Thermography using Deep Learning

Breast Cancer Image Classification Obtained Through Dynamic ...

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science and Information Technology in Smart Administration (ICSINTESA), International Conference of

作者： Ethan Daniel Timadius Reyner Wongso Thirza Baihaqi Alexander Agung Santoso Gunawan Karli Eka Setiawan Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

This paper addresses the vital task of diagnosing breast cancer early and accurately. It acknowledges the difficulty in detecting minor variations in breast cancer patterns through visual examination alone, particularly when analyzing gray scale images. To overcome this challenge, our research concentrates on using image processing models, particularly Convolutional Neural Networks (CNNs), and enhances them with a refined approach to classification. The research methodology encompasses rigorous data collection, preprocessing, and strategic feature selection to enhance model robustness. To address challenges related to data intricacies, the study incorporates transfer learning, refining the model’s training process for improved accuracy. The paper not only seeks to elevate diagnostic precision and minimize errors but also aims to provide healthcare professionals with efficient tools for breast cancer detection. Building upon prior research, the study underscores the potential of deep learning methodologies in advancing breast cancer diagnosis. Furthermore, the fine-tuning process involves adapting a pre-trained model to the intricacies of breast cancer patterns, optimizing its performance. The ultimate goal is to contribute to the development of reliable diagnostic tools that empower medical professionals in the fight against breast cancer. The paper concludes by summarizing key findings. Additionally, the model achieved an accuracy of 84%, highlighting its effectiveness in breast cancer detection. The paper also emphasizes the broader significance of leveraging deep learning for enhanced breast cancer detection.

关键词： Training Deep learning Accuracy Transfer learning Predictive models Feature extraction Breast cancer data models Robustness Image classification

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Predicting Company Default Risks: A Machine Learning Perspective on Importance Features

Predicting Company Default Risks: A Machine Learning Perspec...

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IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

作者： Fanes Hanestan Fritz Nathaniel Raiyen Dewi Kusuma Alexander Agung Santoso Gunawan Karli Eka Setiawan Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350353464

ISBN: (纸本)9798350353471

The 2008 Global Financial Crisis was a devastating event for financial market investors worldwide, as it came unexpectedly and affected every financial market in the world, causing millions of people to suffer massive loss of value of their pension funds and assets. The trigger cause of this Global Financial Crisis was the default of Lehman Brothers, one of the biggest companies in the United States. This paper is intended to prevent this event from happening again by predicting the most important variables causing company default. Company management and investors could take preventive measures when they see signs of these variables in their company. This experiment uses XGBoost, Support Vector Machine, and Random Forest machine learning model, with data retrieved from the Taiwan Economic Journal from 1999 to 2009. The experiment suggests the top 5 most important variables regarding company default are Continuous interest rate (after tax), Persistent EPS in the Last Four Seasons, Total debt/Total net worth, Interest Expense Ratio, and Net Value per Share, with an importance score of 0.447, 0.104, 0.038, 0.033, and 0.032, respectively. These findings show that features related to a company's profitability and liabilities have a strong impact on a company's risk of defaulting.

关键词： Support vector machines Profitability Economic indicators Finance Companies Predictive models data models

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Machine Learning for Imbalanced data in Telecom Churn Classification

Machine Learning for Imbalanced Data in Telecom Churn Classi...

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Information Technology Research and Innovation (ICITRI), International Conference on

作者： Olivia Intan Permata Dewi Vina Nathania Santiko Snow White Putri Safa Alexander Agung Santoso Gunawan Karli Eka Setiawan Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350376210

ISBN: (纸本)9798350376227

In the highly competitive telecommunications industry, customer churn poses a significant threat to revenue and long-term sustainability. To proactively address this challenge, machine learning (ML) techniques offer promising solutions for predicting and mitigating customer churn. This study applies ML algorithms to churn prediction, focusing on the challenges of imbalanced data. Using a publicly available Telecom dataset from Kaggle, the data was meticulously prepared through a multi-stage process, including Random Under-Sampling (RUS) to address class imbalance. The study employed a range of ML algorithms, evaluating their performance using key metrics such as recall, precision, F1score, and accuracy. The findings reveal valuable insights into how RUS can enhance model performance by improving the prediction of the minority class. XGBoost outperformed Logistic Regression and Random Forest in accurately identifying positive outcomes, suggesting its effectiveness in recognizing true instances of customer churn. The confusion matrix supports this observation, showing that the recall value produced by XGBoost slightly exceeded those for Logistic Regression and Random Forest.

关键词： Logistic regression Analytical models Technological innovation Accuracy Predictive models Prediction algorithms data models Loss measurement Telecommunications Random forests

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