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Chen J, Liu A, Zhang H, Yang S, Zheng H, Zhou N, Li P. Improved adaptive-phase fuzzy high utility pattern mining algorithm based on tree-list structure for intelligent decision systems. Sci Rep 2024; 14:945. [PMID: 38200028 PMCID: PMC10781990 DOI: 10.1038/s41598-023-50375-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024] Open
Abstract
With the rapid development of AI and big data mining technologies, computerized medical decision-making has become increasingly prominent. The aim of high-utility pattern mining (HUPM) is to discover meaningful patterns in medical databases that contribute to maximizing the utility from the perspective of diagnosis. However, HUPM pays less attention to the interpretability and explainability of these patterns in medical decision-making scenarios. This paper proposes a novel algorithm called the Improved fuzzy high-utility pattern mining (IF-HUPM) to address this problem. First, the paper applies a fuzzy preprocessing method to divide the fuzzy intervals of a medical quantitative data set, which enhances the fuzziness and interpretability of the data. Next, in the process of IF-HUPM, both fuzzy tree and list structures are employed to calculate fuzzy high-utility values. By combining the characteristics of the one-stage and two-stage algorithms of HUPM, an adaptive-phase Fuzzy HUPM hybrid frame is proposed. The experimental results demonstrate that the proposed IF-HUPM algorithm enhances both accuracy and efficiency and the mining process requires less time and space on average.
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Affiliation(s)
- Jing Chen
- School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
- Baotou Teachers' College of Inner Mongolia University of Science and Technology, Baotou, 014030, Inner Mongolia, China
| | - Aijun Liu
- Baotou Teachers' College of Inner Mongolia University of Science and Technology, Baotou, 014030, Inner Mongolia, China
| | - Hongjun Zhang
- School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Shengyi Yang
- School of Physics and Mechatronic Engineering, Guizhou Minzu University, Guiyang, 550025, China
| | - Hui Zheng
- Software and Computational Systems Program, Data 61, CSIRO, Canberra, Australia
| | - Ning Zhou
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Peng Li
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.
- Institute of Network Security and Trusted Computing, Nanjing, 210023, China.
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Fuzzy-driven Periodic Frequent Pattern Mining. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Debnath S, Ahmed MM, Belhaouari SB, Amagasa T, Rahman M. Buffer-based adaptive fuzzy classifier. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04155-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Feng Q, Pan JS, Du ZG, Peng YJ, Chu SC. Multi-strategy improved parallel antlion algorithm and applied to feature selection. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-219315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Antlion Optimization Algorithm (ALO) is a promising bionic swarm intelligence algorithm, which has good robustness and convergence, but there are still many areas to be improved and modified. Aiming at the fact that the ALO algorithm is more likely to fall into the local optimum, proposes three strategies to improve the classic ALO algorithm in this paper. First of all, we adopt a parallel idea in the algorithm, through the communication strategy between groups based on Quantum-Behaved to enhance the diversity of the population. Secondly, we adopted two strategies, Opposition Learning, and Gaussian Mutation, to balance the performance of exploration and exploitation during the execution of the algorithm, further formed the MSALO algorithm. The CEC2013 Benchmark function is selected as the standard, and MSALO is compared with other intelligent optimization algorithms. The experimental results show that MSALO has stronger optimization performance compared with other intelligent algorithms. Besides, we applied MSALO to the practical scenarios of feature selection, and use SVM classifiers as training evaluators to improve the accuracy of feature extraction from high-dimensional data.
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Affiliation(s)
- Qing Feng
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Zhi-Gang Du
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Yan-jun Peng
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
- College of Science and Engineering, Flinders University, Clovelly Park, SA, Australia
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Jia HD, Li W, Pan JS, Chai QW, Chu SC. Multi-group multi-verse optimizer for energy efficient for routing algorithm in wireless sensor network. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-219313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Wireless sensor network (WSN) is a network composed of a group of wireless sensors with limited energy. With the proliferation of sensor nodes, organization and management of sensor nodes become a challenging task. In this paper, a new topology is proposed to solve the routing problem in wireless sensor networks. Firstly, the sensor nodes are layered to avoid the ring path between cluster heads. Then the nodes of each layer are clustered to facilitate the integration of information and reduce energy dissipation. Moreover, we propose efficient multiverse optimization to mitigate the impact of local optimal solution prematurely and the population diversity declines prematurely. Extensive empirical studies on the CEC 2013 benchmark demonstrate the effectiveness of our new approach. The improved algorithm is further combined with the new topology to handle the routing problem in wireless sensor networks. The energy dissipation generated in routing is significantly lower than that of Multi-Verse Optimizer, Particle Swarm Optimization, and Parallel Particle Swarm Optimization in a wireless sensor network consisting of 5000 nodes.
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Affiliation(s)
- Han-Dong Jia
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Wei Li
- Faculty of the Built Environment, The University of New South Wales, NSW, Australia
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Qing-Wei Chai
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
- College of Science and Engineering, Flinders University, Clovelly Park, SA, Australia
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A One-Phase Tree-Structure Method to Mine High Temporal Fuzzy Utility Itemsets. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12062821] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Compared to fuzzy utility itemset mining (FUIM), temporal fuzzy utility itemset mining (TFUIM) has been proposed and paid attention to in recent years. It considers the characteristics of transaction time, sold quantities of items, unit profit, and transformed semantic terms as essential factors. In the past, a tree-structure method with two phases was previously presented to solve this problem. However, it spent much time because of the number of candidates generated. This paper thus proposes a one-phase tree-structure method to find the high temporal fuzzy utility itemsets in a temporal database. The tree was designed to maintain candidate 1-itemsets with their upper bound values meeting the defined threshold constraint. Besides, each node in this tree keeps the required data of a 1-itemset for mining. We also designed an algorithm to construct the tree and gave an example to illustrate the mining process in detail. Computational experiments were conducted to demonstrate the one-phase tree-structure method is better than the previous one regarding the execution time on three real datasets.
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Nguyen H, Le T, Nguyen M, Fournier-Viger P, Tseng VS, Vo B. Mining frequent weighted utility itemsets in hierarchical quantitative databases. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2021.107709] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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Wang T, Zhou M. Integrating rough set theory with customer satisfaction to construct a novel approach for mining product design rules. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-201829] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
When users choose a product, they consider the emotional experience triggered by the product form. In view of the fact that traditional kansei engineering can not effectively reflect the complex and changeable psychological factors of users, and it has not explored the complex relationship between customer satisfaction and perceptual demand characteristics. To address this problem, some uncertainty techniques including rough sets and fuzzy sets are applied to capture more accurate emotion knowledge. Therefore, this research proposes an integrated evaluation gird method (EGM), rough set theory (RST), continuous fuzzy kano model (CFKM), fuzzy weighted association rule mining method to extract the significant relationship between user needs and product morphological features. The EGM is applied to analyze the attractive factor of morphological characteristics of the product, and then the demand items with the highest satisfaction are analyzed through CFKM. The semantic difference method is combined to construct a decision table, and through attribute reduction and importance calculation to obtain the weight of the core product design items. In order to explore the non-linear relationship between design elements and kansei images, the fuzzy weighted association rule mining method was applied to obtain the set of frequent fuzzy weighted association rules based on evidence theory’s reliability indices of minimum support and confidence so as to realize user demand-driven product design. Taking the design of electric bicycle as an example, the experiment results show that the proposed method can help companies or designers develop products to generate good solutions for customer need.
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Affiliation(s)
- Tianxiong Wang
- School of Art Design and Media, East China University of Science and Technology, Shanghai, China
| | - Meiyu Zhou
- School of Art Design and Media, East China University of Science and Technology, Shanghai, China
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