Article
Computer Science, Theory & Methods
Diana Laura Aguilar, Octavio Loyola-Gonzalez, Miguel Angel Medina-Perez, Leonardo Canete-Sifuentes, Kim-Kwang Raymond Choo
Summary: This paper presents a novel pattern-based classifier, PBC4occ, for one-class classification problems, and introduces the first contrast pattern mining algorithm utilizing decision trees for one-class classification. The results show that PBC4occ achieves the best average values for both AUC and EER metrics on imbalanced databases. The proposal also ranks highest and second-best in Friedman's ranking under EER and AUC evaluation, respectively.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Gelin Zhang, Zhe Hou, Yanhong Huang, Jianqi Shi, Hadrien Bride, Jin Song Dong, Yongsheng Gao
Summary: This study proposes an approach called OptExplain, which extracts global explanations of ensemble trees using logical reasoning, sampling, and nature-inspired optimization. They also introduce ProClass, a method that further simplifies the explanation by solving the MAX-SAT problem. Experimental results show that their approach provides high-quality explanations to large ensemble tree models and outperforms recent top-performers.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Angel Delgado-Panadero, Beatriz Hernandez-Lorca, Maria Teresa Garcia-Ordas, Jose Alberto Benitez-Andrades
Summary: This paper proposes a feature contribution method for GBDT, which can calculate the contribution of each feature to predictions. The method not only serves as a local explainability model for GBDT, but also reflects its internal behavior. It is significant for ethical analysis of AI and compliance with relevant regulations.
INFORMATION SCIENCES
(2022)
Article
Biology
Marcos Antonio Alves, Giulia Zanon Castro, Bruno Alberto Soares Oliveira, Leonardo Augusto Ferreira, Jaime Arturod Ramirez, Rodrigo Silva, Frederico Gadelha Guimaraes
Summary: This paper presents solutions based on Machine Learning techniques for COVID-19 screening in routine blood tests, achieving promising results using a Random Forest classifier. The proposed methodology can help clinicians understand patterns among individuals through analysis of blood parameters on a case-by-case basis.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Computer Science, Artificial Intelligence
Pedro Jose Pereira, Paulo Cortez, Rui Mendes
Summary: The increasing value of Mobile Performance Marketing due to the widespread adoption of mobile devices has led to the development of a data-driven model for predicting user conversion. A novel Multi-objective Optimization approach using Decision Trees evolved through Grammatical Evolution has shown competitive results in handling big data from real-world Demand-Side Platforms, with affordable training times and fast predictive response times.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Yixing Lan, Xin Xu, Qiang Fang, Yujun Zeng, Xinwang Liu, Xianjian Zhang
Summary: This paper proposes a transfer reinforcement learning approach using auto-pruned decision trees for meta-knowledge extraction. Pre-trained policies are learned in source MDPs using RL algorithms, and meta-knowledge is extracted by re-training an auto-pruned decision tree model. In target MDPs, a hybrid policy integrating meta-knowledge and policies learned on the target MDPs is generated. Experimental results demonstrate that the proposed approach outperforms other baselines in terms of learning efficiency and interpretability.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Geochemistry & Geophysics
Hengwei Zhao, Yanfei Zhong, Xinyu Wang, Hong Shu
Summary: In this article, a weakly supervised deep HSI one-class classifier, called HOneCls, is proposed, where a risk estimator-the one-class risk estimator-is particularly introduced to make the fully convolutional neural network (FCN) with the ability of one class classification in the case of distribution imbalance. Extensive experiments (20 tasks in total) were conducted to demonstrate the superiority of the proposed classifier.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Information Science & Library Science
Marina Johnson, Abdullah Albizri, Antoine Harfouche, Samuel Fosso-Wamba
Summary: Artificial intelligence (AI) has gained attention for its potential to reduce costs, increase revenue, and improve customer satisfaction. However, the lack of labeled datasets and the opaque nature of AI algorithms hinder effective decision-making. In this study, the authors propose an approach called informed AI (IAI) that integrates human domain knowledge to develop reliable data labeling and model explainability processes.
INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT
(2022)
Article
Computer Science, Artificial Intelligence
Minyoung Lee, Joohyoung Jeon, Hongchul Lee
Summary: In this study, explainable artificial intelligence techniques were used to analyze the predicted results of a DL model for defect image data, producing human-understandable results through visualization and conversion of prediction results. This approach provided domain experts with reliability and interpretability regarding defect classification.
JOURNAL OF INTELLIGENT MANUFACTURING
(2022)
Article
Computer Science, Hardware & Architecture
Diana Laura Aguilar, Miguel Angel Medina-Perez, Octavio Loyola-Gonzalez, Kim-Kwang Raymond Choo, Edoardo Bucheli-Susarrey
Summary: The importance of understanding and explaining the classification results in AI applications has led to a shift towards explainable AI. This article presents an interpretable autoencoder based on decision trees for categorical data, offering natural explanations for experts. Experimental findings demonstrate its effectiveness as a top-ranked anomaly detection algorithm, outperforming other models.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2023)
Article
Computer Science, Information Systems
Luyl-Da Quach, Khang Nguyen Quoc, Anh Nguyen Quynh, Nguyen Thai-Nghe, Tri Gia Nguyen
Summary: Explainable Artificial Intelligence is a research direction that aims to explain the results of deep learning models. The research proposes two stages in the application process, including evaluating the accuracy of deep learning models and using Grad-CAM for model interpretation. The research results contribute to the construction of intelligent agricultural support systems.
Article
Computer Science, Information Systems
Sejong Oh
Summary: This paper proposes a new method for measuring feature importance and interaction. For the classification model, cases with correct predictions are grouped based on their characteristics, while for the regression model, cases are grouped based on the change in prediction error. The proposed method supports understanding of feature importance and interaction, and decomposes feature importance into feature power and feature interactions.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Josep Alos, Carlos Ansotegui, Eduard Torres
Summary: We propose an approach to enhance the trade-off between accuracy and interpretability of Machine Learning Decision Trees by applying Maximum Satisfiability technology to compute Minimum Pure Decision Trees. We improve the runtime of previous methods and demonstrate that these Minimum Pure Decision Trees can outperform the accuracy of DTs generated with the ML framework sklearn.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Su Nguyen, Binh Tran
Summary: This paper proposes a new approach called XMAP for developing AI systems that can provide accuracy and explanations. XMAP is highly modularised and provides interpretability for each step, achieving competitive predictive performance in classification tasks.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Valerio La Gatta, Vincenzo Moscato, Marco Postiglione, Giancarlo Sperli
Summary: In this paper, a novel model-agnostic Explainable AI technique named CASTLE is proposed to provide rule-based explanations based on both the local and global model's workings. The framework has been evaluated on six datasets in terms of temporal efficiency, cluster quality and model significance, showing a 6% increase in interpretability compared to another state-of-the-art technique, Anchors.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Review
Computer Science, Artificial Intelligence
Sarah Itani, Fabian Lecron, Philippe Fortemps
EXPERT SYSTEMS WITH APPLICATIONS
(2019)
Article
Multidisciplinary Sciences
Sarah Itani, Mandy Rossignol, Fabian Lecron, Philippe Fortemps
Article
Computer Science, Interdisciplinary Applications
Souhir Ben Souissi, Mourad Abed, Lahcen El Hiki, Philippe Fortemps, Marc Pirlot
JOURNAL OF BIOMEDICAL INFORMATICS
(2019)
Editorial Material
Psychiatry
Sarah Itani, Mandy Rossignol
FRONTIERS IN PSYCHIATRY
(2020)
Article
Green & Sustainable Science & Technology
Remi Chauvy, Renato Lepore, Philippe Fortemps, Guy De Weireld
SUSTAINABLE PRODUCTION AND CONSUMPTION
(2020)
Article
Computer Science, Artificial Intelligence
Sonda Elloumi, Taicir Loukil, Philippe Fortemps
Summary: This paper investigates the mode change disruption in the Multi-mode Resource-Constrained Project Scheduling Problem (MRCPSP) and proposes a reactive mathematical modeling. Additionally, three heuristics with several variants are suggested to repair the initial disrupted schedule, with experimental results showing that these methods outperform Cplex in terms of CPU running time.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Landelin Delcoucq, Thomas Dupiereux-Fettweis, Fabian Lecron, Philippe Fortemps
Summary: This paper focuses on the resource perspective of process mining, particularly on clustering resources that share the same behaviors. The authors propose an algorithm that combines the resource perspective and the cell formation approach to identify subgroups of resources performing similar activities and subgroups of activities performed by common resources. The new hierarchical approach provides unique insights into the clustering problem due to its bi-dimensional clustering. Experiments are conducted on both synthetic and real data.
APPLIED INTELLIGENCE
(2023)
Article
Business
Santo Raneri, Fabian Lecron, Julie Hermans, Francois Fouss
Summary: Research on using artificial intelligence (AI) in entrepreneurship is still in its early stages. This study develops and tests a predictive model that provides entrepreneurs with a digital infrastructure for automated testing. The model integrates recommendation algorithm techniques and considers both prediction and controlled logics of action. It is found to be able to predict the desirability level of newly implemented product design decisions in a digital product context. The model also helps detect cases where predictions are less precise and provides an easy-to-assess indicator for product design desirability. This research contributes to the understanding of AI in entrepreneurship and can serve as a starting point for further research in this area.
INTERNATIONAL JOURNAL OF ENTREPRENEURIAL BEHAVIOR & RESEARCH
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Landelin Delcoucq, Fabian Lecron, Philippe Fortemps, Wil M. P. van der Aalst
PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING (SAC'20)
(2020)
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)