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Classification of Incomplete Data Based on Evidence Theory and an Extreme Learning Machine in Wireless Sensor Networks

Yang Zhang, Yun Liu, Han-Chieh Chao, Zhenjiang Zhang, Zhiyuan Zhang
2018 Sensors  
of the distance between interval numbers.  ...  This method uses the regularized extreme learning machine to estimate the potential values of missing data at first, and then it converts the estimations into multiple classification results on the basis  ...  These attribute intervals are treated as interval numbers (INs).  ... 
doi:10.3390/s18041046 pmid:29601552 pmcid:PMC5948797 fatcat:frxqbhfztvctlmgdqjdlxpuo7u

Sensing Attribute Weights: A Novel Basic Belief Assignment Method

Wen Jiang, Miaoyan Zhuang, Chunhe Xie, Jun Wu
2017 Sensors  
To address this issue, a method to determine BBA is proposed based on the attribute weights.  ...  In this paper, based on attribute weights, a novel method to determine BBA is proposed not only in the closed world, but also in the open world.  ...  fuzzy number model of the rth compound subsect proposition {AB} in the jth attribute; S(x) is the area of the x.  ... 
doi:10.3390/s17040721 pmid:28358325 pmcid:PMC5421681 fatcat:bwdgwtqp7fcdboysvjo4l2tyx4

A Reliability-Based Method to Sensor Data Fusion

2017 Sensors  
Originating from the idea of the Z-number, a new method to represent BBAs along with their associated reliability is proposed in this paper, which is named reliability-based BBA.  ...  However, in the µBBA method, the fuzzy membership function is used to represent the knowledge of possibility, and the proposition is modeled by an interval.  ...  The advantages of our method are concluded as follows: • Based on the idea of the Z-number, an ordered pair (BBA, R) is proposed to represent BBA along with its associated reliability.  ... 
doi:10.3390/s17071575 pmid:28678179 pmcid:PMC5539540 fatcat:albhqrpoqzg35kdx5gpxvbyrcy

An improved method to transform triangular fuzzy number into basic belief assignment in evidence theory

Tianshuo Ma, Fuyuan Xiao
2019 IEEE Access  
Since the fuzzy number is useful to construct the target model for generating basic belief assignments, in this paper, an improved method to obtain basic belief assignment is proposed based on the triangular  ...  First, the k-means++ clustering method is used to construct the target model.  ...  [50] proposed a method to transform interval number model into the BBAs.  ... 
doi:10.1109/access.2019.2900362 fatcat:u5z3moowczgoremrnctqyycyh4

Belief Entropy Tree and Random Forest: Learning from Data with Continuous Attributes and Evidential Labels

Kangkai Gao, Yong Wang, Liyao Ma
2022 Entropy  
With the Gaussian mixture model, this tree method is able to deal with continuous attribute values directly, without pretreatment of discretization.  ...  Specifically, the tree method adopts belief entropy, a kind of uncertainty measurement based on the basic belief assignment, as a new attribute selection tool.  ...  attribute is divided into four intervals of equal width before learning.  ... 
doi:10.3390/e24050605 fatcat:thppombtybf4xgobclhkxbshcu

Short-Term Wind Speed Forecasting Based on Ensemble Online Sequential Extreme Learning Machine and Bayesian Optimization

Jicheng Quan, Li Shang, Yang Li
2020 Mathematical Problems in Engineering  
The performances of the proposed model were compared with various representative models. The experimental results indicate that the proposed model has better accuracy than the comparison models.  ...  This paper aims to propose a hybrid forecasting approach of short-term wind speed based on a novel signal processing algorithm, a wrapper-based feature selection method, the state-of-art optimization algorithm  ...  Each data set contains 1800 points with 10 min interval.  ... 
doi:10.1155/2020/7212368 fatcat:hp5h7qn4grgchcsqsc2rkjsvfa

Research on the Fusion of Dependent Evidence Based on Rank Correlation Coefficient

Fengjian Shi, Xiaoyan Su, Hong Qian, Ning Yang, Wenhua Han
2017 Sensors  
In this paper, an innovative evidence fusion model to deal with dependent evidence based on rank correlation coefficient is proposed.  ...  An example is illustrated to show the use and effectiveness of the proposed method.  ...  Smets proposed a combination method in [51] based on the TBM (transferable belief model).  ... 
doi:10.3390/s17102362 pmid:29035341 pmcid:PMC5676609 fatcat:rhnobmie6faofjju7wyq2hgfti

Decision Support Methods for Finding Phenotype — Disorder Associations in the Bone Dysplasia Domain

Razan Paul, Tudor Groza, Jane Hunter, Andreas Zankl, Andrey Rzhetsky
2012 PLoS ONE  
This paper describes an approach for developing a decision support model in medical domains that are underpinned by relatively sparse knowledge bases.  ...  We show, via experimental results, that our approach is able to provide meaningful outcomes even on small datasets with sparse distributions, in addition to outperforming other Machine Learning techniques  ...  Based on the training data, the decision tree learning algorithm constructs a tree-shaped model using inductive reasoning.  ... 
doi:10.1371/journal.pone.0050614 pmid:23226331 pmcid:PMC3511538 fatcat:7ci5ixiehrh5lc4qy6eg6jw2ty

Evidence combination based on credal belief redistribution for pattern classification

Zhunga Liu, Yu Liu, Jean Dezert, Fabio Cuzzolin
2019 IEEE transactions on fuzzy systems  
The effectiveness of CBR is extensively validated on several real datasets from the UCI repository, and critically compared with that of other related fusion methods.  ...  By doing this, one can efficiently reduce the chance of misclassification by modeling partial imprecision.  ...  Comparison with boosting Two popular ensemble learning methods, i.e.  ... 
doi:10.1109/tfuzz.2019.2911915 fatcat:7hk3k44n2ne3rg4oqahgiik62m

An Evidence-Theoretic k-Nearest Neighbor Rule for Multi-label Classification [chapter]

Zoulficar Younes, Fahed Abdallah, Thierry Denœux
2009 Lecture Notes in Computer Science  
The proposed method generalizes an existing single-label evidence-theoretic learning method to the multi-label case.  ...  In multi-label learning, each instance in the training set is associated with a set of labels, and the task is to output a label set for each unseen instance.  ...  The model parameters for EM L − kN N are : The number of neighbors k, and the parameters for the induced BBAs, α, β and γ. M L − KN N has only one parameter that needs to be optimized, which is k.  ... 
doi:10.1007/978-3-642-04388-8_23 fatcat:7yegwz5ybndfney7ueoktri7ma

Rough Set Classifier Based on DSmT

Yilin Dong, Xinde Li, Jean Dezert
2018 2018 21st International Conference on Information Fusion (FUSION)  
It can be experimentally verified that our proposed approach can deal efficiently with the uncertainty in rough set classifiers.  ...  However, some classes of targets cannot be determined when multiple categories cannot be easily distinguished (for example, the number of votes of different classes is the same).  ...  Referring to the construction methods of BBAs in [21] , [22] , [23] , we propose in this paper a new construction method for the BBA based on so-called attribute polygon in RST.  ... 
doi:10.23919/icif.2018.8455552 dblp:conf/fusion/DongLD18 fatcat:pd3px3py5ne4xmfegifvc4ma74

An Integrated Model for Robust Multisensor Data Fusion

Bo Shen, Yun Liu, Jun-Song Fu
2014 Sensors  
The proposed model is based on the connection of Dempster-Shafer evidence theory and an extreme learning machine.  ...  source from several mass functions or experts; and a new way to make high-precision decisions based on an extreme learning machine (ELM).  ...  where ( ) denotes the accuracy rate with respect to class , N is the total BBA number of the test sets. is the number of accurate BBAs.  ... 
doi:10.3390/s141019669 pmid:25340445 pmcid:PMC4239917 fatcat:gpjuumjj2fe4bgrqzhtn2hwf3m

Learning a Low-Dimensional Representation of a Safe Region for Safe Reinforcement Learning on Dynamical Systems

Zhehua Zhou, Ozgur S. Oguz, Marion Leibold, Martin Buss
2021 IEEE Transactions on Neural Networks and Learning Systems  
Based on the simplified system model, a low-dimensional representation of the safe region is identified and used to provide safety estimates for learning algorithms.  ...  For the safe application of reinforcement learning algorithms to high-dimensional nonlinear dynamical systems, a simplified system model is used to formulate a safe reinforcement learning (SRL) framework  ...  For uncertain dynamical systems, methods based on learning a model of unknown system dynamics [13] or of environmental constraints [14] are proposed to ensure safety during the learning.  ... 
doi:10.1109/tnnls.2021.3106818 pmid:34478385 fatcat:du533syv6vfazcjtkfsl5gayr4

A Binary Bat Inspired Algorithm for the Classification of Breast Cancer Data

Doreswamy, Umme Salma M
2016 International Journal on Soft Computing Artificial Intelligence and Applications  
The authors propose a Binary Bat Algorithm (BBA) based Feedforward Neural Network (FNN) hybrid model, where the advantages of BBA and efficiency of FNN is exploited for the classification of three benchmark  ...  FNNBBA based classification produces 92.61% accuracy for training data and 89.95% for testing data.  ...  model with change in number of iterations.  ... 
doi:10.5121/ijscai.2016.5301 fatcat:psopgcsm5zakdmhs6ksbnpmieu

Credal Transfer Learning with Multi-estimation for Missing Data

Zongfang Ma, Zhe Liu, Yiru Zhang, Lin Song, Jihuan He
2020 IEEE Access  
To address this problem, we propose credal transfer learning (CTL) with multi-estimation for missing data based on belief function theory by introducing uncertainty and imprecision in data imputation procedure  ...  However, the classification of missing data (or incomplete data) is a challenging task for TL because different strategies of imputation may have strong impacts on learning models.  ...  A number of methods [17] , [18] have been developed to deal with traditional classification problems with missing data.  ... 
doi:10.1109/access.2020.2983319 fatcat:53kerpj6yjc4db7npmmplavwrq
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