IEEE Transactions on Emerging Topics in Computational Intelligence最新文献_第10页

Evolutionary Biparty Multiobjective UAV Path Planning: Problems and Empirical Comparisons 进化双方多目标无人机路径规划：问题与经验比较

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-12 DOI: 10.1109/TETCI.2024.3361755

Kesheng Chen;Wenjian Luo;Xin Lin;Zhen Song;Yatong Chang

{"title":"Evolutionary Biparty Multiobjective UAV Path Planning: Problems and Empirical Comparisons","authors":"Kesheng Chen;Wenjian Luo;Xin Lin;Zhen Song;Yatong Chang","doi":"10.1109/TETCI.2024.3361755","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3361755","url":null,"abstract":"Unmanned aerial vehicles (UAVs) have been widely used in urban missions, and proper planning of UAV paths can improve mission efficiency while reducing the risk of potential third-party impact. Existing work has considered all efficiency and safety objectives for a single decision-maker (DM) and regarded this as a multiobjective optimization problem (MOP). However, there is usually not a single DM but two DMs, i.e., an efficiency DM and a safety DM, and the DMs are only concerned with their respective objectives. The final decision is made based on the solutions of both DMs. In this paper, for the first time, biparty multiobjective UAV path planning (BPMO-UAVPP) problems involving both efficiency and safety departments are modeled. The existing multiobjective immune algorithm with nondominated neighbor-based selection (NNIA), the hybrid evolutionary framework for the multiobjective immune algorithm (HEIA), and the adaptive immune-inspired multiobjective algorithm (AIMA) are modified for solving the BPMO-UAVPP problem, and then biparty multiobjective optimization algorithms, including the BPNNIA, BPHEIA, and BPAIMA, are proposed and comprehensively compared with traditional multiobjective evolutionary algorithms and typical multiparty multiobjective evolutionary algorithms (i.e., OptMPNDS and OptMPNDS2). The experimental results show that BPAIMA performs better than ordinary multiobjective evolutionary algorithms such as NSGA-II and multiparty multiobjective evolutionary algorithms such as OptMPNDS, OptMPNDS2, BPNNIA and BPHEIA.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 3","pages":"2433-2445"},"PeriodicalIF":5.3,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

KGCNA: Knowledge Graph Collaborative Neighbor Awareness Network for Recommendation KGCNA：用于推荐的知识图谱协作邻居认知网络

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-12 DOI: 10.1109/TETCI.2024.3369976

Guangliang He;Zhen Zhang;Hanrui Wu;Sanchuan Luo;Yudong Liu

{"title":"KGCNA: Knowledge Graph Collaborative Neighbor Awareness Network for Recommendation","authors":"Guangliang He;Zhen Zhang;Hanrui Wu;Sanchuan Luo;Yudong Liu","doi":"10.1109/TETCI.2024.3369976","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3369976","url":null,"abstract":"Knowledge graph (KG) is increasingly important in improving recommendation performance and handling item cold-start. A recent research hotspot is designing end-to-end models based on information propagation schemes. However, existing these methods do not highlight key collaborative signals hidden in user-item bipartite graphs, which leads to two problems: (1) the collaborative signal of user collaborative neighbors is not modeled and (2) the incompleteness of KG and the behavioral similarity of item collaborative neighbors are not considered. In this paper, we design a new model called \u0000<italic>Knowledge Graph Collaborative Neighbor Awareness network</i>\u0000 (KGCNA) in order to resolve the above problems. KGCNA models the top-k collaborative neighbors of users and items to extract the collaborative preference of the user's top-k collaborative neighbors, the missing attributes of items, and the behavioral similarity of the item's top-k collaborative neighbors, respectively. At the same time, KGCNA designs a novel information aggregation method, which adopts different aggregation methods for users and items to capture the user's item-based behavior preference and the item's long-distance knowledge association in KG, respectively. Furthermore, KGCNA uses an information-gated aggregation mechanism to extract discriminative signals to better study user behavior intent. Experimental results on three benchmark datasets demonstrate that KGCNA significantly improves over state-of-the-art techniques such as CKAN, KGIN, and KGAT.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 4","pages":"2736-2748"},"PeriodicalIF":5.3,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Model-Based Off-Policy Deep Reinforcement Learning With Model-Embedding 基于模型的政策外深度强化学习与模型嵌入

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-12 DOI: 10.1109/TETCI.2024.3369636

Xiaoyu Tan;Chao Qu;Junwu Xiong;James Zhang;Xihe Qiu;Yaochu Jin

{"title":"Model-Based Off-Policy Deep Reinforcement Learning With Model-Embedding","authors":"Xiaoyu Tan;Chao Qu;Junwu Xiong;James Zhang;Xihe Qiu;Yaochu Jin","doi":"10.1109/TETCI.2024.3369636","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3369636","url":null,"abstract":"Model-based reinforcement learning (MBRL) has shown its advantages in sample efficiency over model-free reinforcement learning (MFRL) by leveraging control-based domain knowledge. Despite the impressive results it achieves, MBRL is still outperformed by MFRL due to the lack of unlimited interactions with the environment. While imaginary data can be generated by imagining the trajectories of future states, a trade-off between the usage of data generation and the influence of model bias remains to be resolved. In this paper, we propose a simple and elegant off-policy model-based deep reinforcement learning algorithm with a model embedded in the framework of probabilistic reinforcement learning, called MEMB. To balance the sample-efficiency and model bias, we exploit both real and imaginary data in training. In particular, we embed the model in the policy update and learn value functions from the real data set. We also provide a theoretical analysis of MEMB with the Lipschitz continuity assumption on the model and policy, proving the reliability of the short-term imaginary rollout. Finally, we evaluate MEMB on several benchmarks and demonstrate that our algorithm can achieve state-of-the-art performance.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 4","pages":"2974-2986"},"PeriodicalIF":5.3,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Genetic Programming for Feature Selection Based on Feature Removal Impact in High-Dimensional Symbolic Regression 基于高维符号回归中特征去除影响的特征选择遗传编程

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-11 DOI: 10.1109/TETCI.2024.3369407

Baligh Al-Helali;Qi Chen;Bing Xue;Mengjie Zhang

{"title":"Genetic Programming for Feature Selection Based on Feature Removal Impact in High-Dimensional Symbolic Regression","authors":"Baligh Al-Helali;Qi Chen;Bing Xue;Mengjie Zhang","doi":"10.1109/TETCI.2024.3369407","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3369407","url":null,"abstract":"Symbolic regression is increasingly important for discovering mathematical models for various prediction tasks. It works by searching for the arithmetic expressions that best represent a target variable using a set of input features. However, as the number of features increases, the search process becomes more complex. To address high-dimensional symbolic regression, this work proposes a genetic programming for feature selection method based on the impact of feature removal on the performance of SR models. Unlike existing Shapely value methods that simulate feature absence at the data level, the proposed approach suggests removing features at the model level. This approach circumvents the production of unrealistic data instances, which is a major limitation of Shapely value and permutation-based methods. Moreover, after calculating the importance of the features, a cut-off strategy, which works by injecting a number of random features and utilising their importance to automatically set a threshold, is proposed for selecting important features. The experimental results on artificial and real-world high-dimensional data sets show that, compared with state-of-the-art feature selection methods using the permutation importance and Shapely value, the proposed method not only improves the SR accuracy but also selects smaller sets of features.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 3","pages":"2269-2282"},"PeriodicalIF":5.3,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Switched Neural Networks for Simultaneous Learning of Multiple Functions 用于同时学习多种功能的开关神经网络

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-11 DOI: 10.1109/TETCI.2024.3369981

Mehmet Önder Efe;Burak Kürkçü;Coşku Kasnakoǧlu;Zaharuddin Mohamed;Zhijie Liu

{"title":"Switched Neural Networks for Simultaneous Learning of Multiple Functions","authors":"Mehmet Önder Efe;Burak Kürkçü;Coşku Kasnakoǧlu;Zaharuddin Mohamed;Zhijie Liu","doi":"10.1109/TETCI.2024.3369981","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3369981","url":null,"abstract":"This paper introduces the notion of switched neural networks for learning multiple functions under different switching configurations. The neural network structure has adjustable parameters and for each function the state of the parameter vector is determined by a mask vector, 1/0 for active/inactive or +1/-1 for plain/inverted. The optimization problem is to schedule the switching strategy (mask vector) required for each function together with the best parameter vector (weights/biases) minimizing the loss function. This requires a procedure that optimizes a vector containing real and binary values simultaneously to discover commonalities among various functions. Our studies show that a small sized neural network structure with an appropriate switching regime is able to learn multiple functions successfully. During the tests focusing on classification, we considered 2-variable binary functions and all 16 combinations have been chosen as the functions. The regression tests consider four functions of two variables. Our studies showed that simple NN structures are capable of storing multiple information via appropriate switching.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 4","pages":"3095-3104"},"PeriodicalIF":5.3,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data Efficient Deep Reinforcement Learning With Action-Ranked Temporal Difference Learning 利用行动排序时差学习实现数据高效深度强化学习

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-11 DOI: 10.1109/TETCI.2024.3369641

Qi Liu;Yanjie Li;Yuecheng Liu;Ke Lin;Jianqi Gao;Yunjiang Lou

{"title":"Data Efficient Deep Reinforcement Learning With Action-Ranked Temporal Difference Learning","authors":"Qi Liu;Yanjie Li;Yuecheng Liu;Ke Lin;Jianqi Gao;Yunjiang Lou","doi":"10.1109/TETCI.2024.3369641","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3369641","url":null,"abstract":"In value-based deep reinforcement learning (RL), value function approximation errors lead to suboptimal policies. Temporal difference (TD) learning is one of the most important methodologies to approximate state-action (\u0000<inline-formula><tex-math>$Q$</tex-math></inline-formula>\u0000) value function. In TD learning, it is critical to estimate \u0000<inline-formula><tex-math>$Q$</tex-math></inline-formula>\u0000 values of greedy actions more accurately because a more accurate target \u0000<inline-formula><tex-math>$Q$</tex-math></inline-formula>\u0000 value enhances the estimation accuracy of \u0000<inline-formula><tex-math>$Q$</tex-math></inline-formula>\u0000 value. To improve the estimation accuracy of \u0000<inline-formula><tex-math>$Q$</tex-math></inline-formula>\u0000 value, we propose an action-ranked TD learning method to enhance the performance of deep RL by weighting each TD error according to the rank of its corresponding state-action pair's value among all the \u0000<inline-formula><tex-math>$Q$</tex-math></inline-formula>\u0000 values on a state. The proposed method can provide more accurate target values for TD learning, making the estimation of the \u0000<inline-formula><tex-math>$Q$</tex-math></inline-formula>\u0000 value more accurate. We apply the proposed method to a representative value-based deep RL algorithm, and results show that the proposed method outperforms baselines on 31 out of 40 Atari games. Furthermore, we extend the proposed method to multi-agent deep RL. To adaptively determine the hyperparameter in action-ranked TD learning, we propose a meta action-ranked TD learning. A series of experiments quantitatively verify that our methods outperform baselines on Atari games, StarCraft-II, and Grid World environments.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 4","pages":"2949-2961"},"PeriodicalIF":5.3,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised Feature Selection via Collaborative Embedding Learning 通过协作嵌入学习进行无监督特征选择

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-11 DOI: 10.1109/TETCI.2024.3369313

Junyu Li;Fei Qi;Xin Sun;Bin Zhang;Xiangmin Xu;Hongmin Cai

{"title":"Unsupervised Feature Selection via Collaborative Embedding Learning","authors":"Junyu Li;Fei Qi;Xin Sun;Bin Zhang;Xiangmin Xu;Hongmin Cai","doi":"10.1109/TETCI.2024.3369313","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3369313","url":null,"abstract":"Unsupervised feature selection is vital in explanatory learning and remains challenging due to the difficulty of formulating a learnable model. Recently, graph embedding learning has gained widespread popularity in unsupervised learning, which extracts low-dimensional representation based on graph structure. Nevertheless, such an embedding scheme for unsupervised feature selection will distort original features due to the spatial transformation by extraction. To address this problem, this paper proposes a collaborative graph embedding model for unsupervised feature selection via jointly using soft-threshold and low-dimensional embedding learning. The former learns a threshold selection matrix for feature weighting in the original space. The latter extracts embedded representation in low-dimensional space to reveal the latent graph structure. By collaborative learning, the proposed method can simultaneously perform unsupervised feature selection in the original space and adaptive graph learning via dual embedding. Extensive experiments on five benchmark datasets demonstrate that the proposed method achieves superior performance compared to eight competing methods.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 3","pages":"2529-2540"},"PeriodicalIF":5.3,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-Modal Learning via Adversarial Loss and Covariate Shift for Enhanced Liver Segmentation 通过对抗损失和变量移动进行跨模态学习以增强肝脏分割能力

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-08 DOI: 10.1109/TETCI.2024.3369868

Savas Ozkan;M. Alper Selver;Bora Baydar;Ali Emre Kavur;Cemre Candemir;Gozde Bozdagi Akar

{"title":"Cross-Modal Learning via Adversarial Loss and Covariate Shift for Enhanced Liver Segmentation","authors":"Savas Ozkan;M. Alper Selver;Bora Baydar;Ali Emre Kavur;Cemre Candemir;Gozde Bozdagi Akar","doi":"10.1109/TETCI.2024.3369868","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3369868","url":null,"abstract":"Despite the widespread use of deep learning methods for semantic segmentation from single imaging modalities, their performance for exploiting multi-domain data still needs to improve. However, the decision-making process in radiology is often guided by data from multiple sources, such as pre-operative evaluation of living donated liver transplantation donors. In such cases, cross-modality performances of deep models become more important. Unfortunately, the domain-dependency of existing techniques limits their clinical acceptability, primarily confining their performance to individual domains. This issue is further formulated as a multi-source domain adaptation problem, which is an emerging field mainly due to the diverse pattern characteristics exhibited from cross-modality data. This paper presents a novel method that can learn robust representations from unpaired cross-modal (CT-MR) data by encapsulating distinct and shared patterns from multiple modalities. In our solution, the covariate shift property is maintained with structural modifications in our architecture. Also, an adversarial loss is adopted to boost the representation capacity. As a result, sparse and rich representations are obtained. Another superiority of our model is that no information about modalities is needed at the training or inference phase. Tests on unpaired CT and MR liver data obtained from the cross-modality task of the CHAOS grand challenge demonstrate that our approach achieves state-of-the-art results with a large margin in both individual metrics and overall scores.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 4","pages":"2723-2735"},"PeriodicalIF":5.3,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Population Structures-Based Differential Evolution Algorithm 基于动态种群结构的差分进化算法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-08 DOI: 10.1109/TETCI.2024.3367809

Jiaru Yang;Kaiyu Wang;Yirui Wang;Jiahai Wang;Zhenyu Lei;Shangce Gao

{"title":"Dynamic Population Structures-Based Differential Evolution Algorithm","authors":"Jiaru Yang;Kaiyu Wang;Yirui Wang;Jiahai Wang;Zhenyu Lei;Shangce Gao","doi":"10.1109/TETCI.2024.3367809","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3367809","url":null,"abstract":"The coordination of population structure is the foundation for the effective functioning of evolutionary algorithms. An efficient population evolution structure can guide individuals to engage in successful and robust exploitative and exploratory behaviors. However, due to the black-box property of the search process, it is challenging to assess the current state of the population and implement targeted measures. In this paper, we propose a dynamic population structures-based differential evolution algorithm (DPSDE) to uncover the real-time state of population continuous optimization. According to the exploitation and exploration state of population, we introduce four structural modules to address the premature convergence and search stagnation issues of the current population. To effectively utilize these modules, we propose a real-time discernment mechanism to judge the population's current state. Based on the feedback information, suitable structural modules are dynamically invoked, ensuring that the population undergoes continuous and beneficial evolution, ultimately exploring the optimal population structure. The comparative outcomes with numerous cutting-edge algorithms on the IEEE Congress on Evolutionary Computation (CEC) 2017 benchmark functions and 2011 real-world problems verify the superiority of DPSDE. Furthermore, parameters, population state, and ablation study of modules are discussed.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 3","pages":"2493-2505"},"PeriodicalIF":5.3,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dendritic Neural Network: A Novel Extension of Dendritic Neuron Model 树突状神经网络：树突状神经元模型的新扩展

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-08 DOI: 10.1109/TETCI.2024.3367819

Cheng Tang;Junkai Ji;Yuki Todo;Atsushi Shimada;Weiping Ding;Akimasa Hirata

{"title":"Dendritic Neural Network: A Novel Extension of Dendritic Neuron Model","authors":"Cheng Tang;Junkai Ji;Yuki Todo;Atsushi Shimada;Weiping Ding;Akimasa Hirata","doi":"10.1109/TETCI.2024.3367819","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3367819","url":null,"abstract":"The conventional dendritic neuron model (DNM) is a single-neuron model inspired by biological dendritic neurons that has been applied successfully in various fields. However, an increasing number of input features results in inefficient learning and gradient vanishing problems in the DNM. Thus, the DNM struggles to handle more complex tasks, including multiclass classification and multivariate time-series forecasting problems. In this study, we extended the conventional DNM to overcome these limitations. In the proposed dendritic neural network (DNN), the flexibility of both synapses and dendritic branches is considered and formulated, which can improve the model's nonlinear capabilities on high-dimensional problems. Then, multiple output layers are stacked to accommodate the various loss functions of complex tasks, and a dropout mechanism is implemented to realize a better balance between the underfitting and overfitting problems, which enhances the network's generalizability. The performance and computational efficiency of the proposed DNN compared to state-of-the-art machine learning algorithms were verified on 10 multiclass classification and 2 high-dimensional binary classification datasets. The experimental results demonstrate that the proposed DNN is a promising and practical neural network architecture.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 3","pages":"2228-2239"},"PeriodicalIF":5.3,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10460122","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0