Article
Computer Science, Artificial Intelligence
Dipanjyoti Paul, Anushree Jain, Sriparna Saha, Jimson Mathew
Summary: This paper presents an adaptive feature selection algorithm for multi-label classification scenarios, real-time selecting the optimal feature subset online. Through a three-phase filtering process, the algorithm improves the accuracy and efficiency of feature selection.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Abdolreza Rashno, Milad Shafipour, Sadegh Fadaei
Summary: This paper introduces a novel multi-objective particle swarm optimization feature selection method. It decodes feature vectors as particles and ranks them in a two-dimensional optimization space. The proposed method incorporates feature ranks to update particle velocity and position during the optimization process. Experimental results demonstrate the effectiveness of the method in finding Pareto Fronts of the best particles in multi-objective optimization space.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Rohit Kundu, Rammohan Mallipeddi
Summary: This paper proposes a hybrid filter multi-objective evolutionary algorithm (HFMOEA) based on the non-dominated sorting genetic algorithm (NSGA-II) coupled with filter-based feature ranking methods. The HFMOEA aims to select an optimal trade-off solution set by minimizing the number of selected features and maximizing the classification accuracy. The proposed method has been evaluated on various datasets to demonstrate its effectiveness.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2022)
Article
Computer Science, Information Systems
Peng Wang, Bing Xue, Jing Liang, Mengjie Zhang
Summary: By identifying relevant features, feature selection methods can maintain or improve classification accuracy and reduce dimensionality. This paper proposes a diversity-based multi-objective differential evolution approach to effectively handle the trade-offs between convergence and diversity. The method detects and removes irrelevant and weakly relevant features to reduce the search space and proposes a new binary mutation operator to produce better feature subsets. Experimental results show that the proposed method outperforms current popular multi-objective feature selection methods on 14 datasets with varying difficulty.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Pradip Dhal, Chandrashekhar Azad
Summary: In this study, a binary version of the hybrid two-phase multi-objective FS approach based on PSO and GWO is proposed. The approach aims to minimize classification error rate and reduce the number of selected features. By utilizing global and local search strategies, the method shows efficient and effective performance in selecting prominent features in high-dimensional data.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Jianfeng Qiu, Xiaoshu Xiang, Chao Wang, Xingyi Zhang
Summary: This paper introduces a new multi-objective chemical reaction optimization algorithm for feature selection, which utilizes the preference information related to the problem to guide the population evolution, aiming to enhance both convergence and diversity of the population. Experimental results demonstrate the superiority of the proposed algorithm over seven existing FS algorithms on 12 benchmark data sets.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Azar Rafie, Parham Moradi, Abdulbaghi Ghaderzadeh
Summary: Multi-label classification methods assign more than one label to each instance. High dimensional problems can reduce the performance of machine learning methods, so feature selection is introduced to choose a small set of prominent features. Traditional multi-label feature selection methods fail when applied on data streams, so online methods are introduced. This paper proposes a multi-objective search strategy using mutual information and Pareto optimal set theories to select streaming features, with the obtained results demonstrating its effectiveness.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Babak Nouri-Moghaddam, Mehdi Ghazanfari, Mohammad Fathian
Summary: The study introduces a multi-objective feature selection algorithm based on the forest optimization algorithm, showing that it can reduce classification errors and feature numbers in most cases.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
An-Da Li, Bing Xue, Mengjie Zhang
Summary: This paper proposes a feature selection method to identify key quality features in complex manufacturing processes. A multi-objective binary particle swarm optimization algorithm is proposed, which includes three new components to optimize a bi-objective feature selection model. Experimental results show that this method can identify a small number of key quality features with good predictive ability.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Rajesh Ranjan, Jitender Kumar Chhabra
Summary: This study proposes a multi-objective crow search algorithm for clustering and feature selection (MO-CSACFS) by modifying the crow search algorithm and introducing a levy flight-based two-point cross-over mechanism. MO-CSACFS is implemented over several datasets to assess its performance, and it is compared with other similar algorithms. The results show that MO-CSACFS produces compact and robust clusters comparable to other works from the literature.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Yu Xue, Haokai Zhu, Jiayu Liang, Adam Slowik
Summary: Feature selection is a crucial pre-processing technique for classification, aiming to enhance classification accuracy by removing irrelevant or redundant features. This study introduces a multi-objective genetic algorithm with an adaptive operator selection mechanism, which effectively addresses high-dimensional feature selection problems.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Hardware & Architecture
Amal Francis Ukken, Arjun Bindu Jayachandran, Jaideep Kumar Punnath Malayathodi, Pranesh Das
Summary: Feature selection is an important step in improving the performance of a classifier. The Binary Multi-Objective Grey Wolf Optimizer-Sigmoid (BMOGWO-S) algorithm has been introduced as a better approach to feature selection compared to other existing algorithms. This algorithm utilizes a metaheuristic method and does not rely on statistical information from the dataset. In this research, the authors propose a Statistically aided Binary Multi-Objective Grey Wolf Optimizer-Sigmoid (SaBMOGWO-S) algorithm that incorporates statistical information and introduces methods to reduce computation time.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Physics, Multidisciplinary
Hongbin Dong, Jing Sun, Xiaohang Sun
Summary: A feature selection algorithm SHAPFS-ML for multi-label learning is proposed, utilizing multi-objective optimization algorithm and Shapley value to identify relevant, redundant, and irrelevant features. Experimental results demonstrate that the algorithm is an effective feature selection method, reducing the computational complexity of classification algorithms and improving classification accuracy.
Article
Engineering, Multidisciplinary
Xiao-Zhi Gao, Madhu Sudana Rao Nalluri, K. Kannan, Diptendu Sinharoy
Summary: The study introduces a hybrid cat swarm optimization (HCSO) algorithm, which shows better performance in feature subset selection compared to the traditional CSO through testing.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Adel Got, Abdelouahab Moussaoui, Djaafar Zouache
Summary: This paper introduces a novel hybrid filter-wrapper feature selection approach using whale optimization algorithm, which optimizes multiple objective functions simultaneously. Experimental results demonstrate the efficiency of the proposed algorithm on twelve benchmark datasets, showing its ability to obtain subsets with smaller number of features and excellent classification accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Mousumi Sardar, Sushmita Mitra, B. Uma Shankar
APPLIED SOFT COMPUTING
(2018)
Article
Computer Science, Information Systems
Subhashis Banerjee, Sushmita Mitra, B. Uma Shankar
INFORMATION SCIENCES
(2018)
Editorial Material
Computer Science, Artificial Intelligence
Sabu M. Thampi, El-Sayed M. El-Alfy, Sushmita Mitra, Ljiljana Trajkovic
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2018)
Editorial Material
Computer Science, Artificial Intelligence
Ponnuthurai N. Suganthan, Sushmita Mitra, Ivan Tyukin
APPLIED SOFT COMPUTING
(2018)
Article
Computer Science, Artificial Intelligence
Soham Mandal, Sushmita Mitra, B. Uma Shankar
Article
Computer Science, Information Systems
Anandarup Mukherjee, Sudip Misra, Narendra Singh Raghuwanshi, Sushmita Mitra
IEEE INTERNET OF THINGS JOURNAL
(2019)
Article
Engineering, Biomedical
Kazeem Adesina Dauda, Biswabrata Pradhan, B. Uma Shankar, Sushmita Mitra
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
(2019)
Article
Computer Science, Artificial Intelligence
Saumya Bhadani, Sushmita Mitra, Subhashis Banerjee
Article
Mathematical & Computational Biology
Subhashis Banerjee, Sushmita Mitra
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
(2020)
Article
Computer Science, Information Systems
Ritaban Kirtania, Subhashis Banerjee, Sayantan Laha, B. Uma Shankar, Raghunath Chatterjee, Sushmita Mitra
Summary: This study introduced a novel Autoencoder-based strategy for predicting survival of GBM patients, showing superior performance in survival group prediction with high accuracy and AUC values. The biological significance of the extracted genes was analyzed to establish their importance, and the statistical significance of the DeepSGP algorithm's predicted output was confirmed using the concordance index.
Article
Engineering, Biomedical
Sushmita Mitra
Summary: This survey introduces multidisciplinary radiogenomic approaches in the diagnosis and personalized management of gliomas, integrating noninvasive imaging with genomic information. It discusses deep learning techniques for automated feature extraction from tumor radioimages and the analysis of gene expression values. The association between genomic and radiomic features is explored using deep learning and transfer learning for knowledge discovery. The literature primarily focuses on brain magnetic resonance imaging (MRI) data and involves databases such as NIH TCIA, TCGA, and BraTS Challenge.
IEEE REVIEWS IN BIOMEDICAL ENGINEERING
(2023)
Article
Computer Science, Information Systems
Mousumi Sardar, Subhashis Banerjee, Sushmita Mitra
Article
Computer Science, Artificial Intelligence
Ritaban Kirtania, Sushmita Mitra, B. Uma Shankar
INTELLIGENT DATA ANALYSIS
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Subhashis Banerjee, Sushmita Mitra, B. Uma Shankar
PROCEEDINGS OF INTERNATIONAL CONFERENCE ON COMPUTER VISION AND IMAGE PROCESSING, CVIP 2016, VOL 1
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Subhashis Banerjee, Sushmita Mitra, B. Uma Shankar
2017 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
(2017)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
