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Showing 1-25 of 3,633 resultsfor "Index Terms":Incomplete Model
"Index Terms":Incomplete Model
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Missing values often occur in real-world datasets, which undermines the data integrity and reduces the reliability of data mining. In this paper, a method of Takagi-Sugeno (TS) fuzzy modeling for incomplete data is proposed and utilized to estimate missing values. Considering the difference of attribute relationship within different clusters, this method performs regression analysis on the subsets...Show More
Survival analysis using neural network hazard model with incomplete covariate data
2011 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering
Year: 2011 | Conference Paper |
This paper presents a new procedure to perform survival analysis when some covariate data are not available. A neural network hazard model is utilized here to model the relationship between covariates and the hazard. In order to consider incomplete covariates, the hidden layer target data are represented to be binary random variables. This will enable the training of the two-layer neural network h...Show More
Research of Model Evolvement Method for Discrete-Event Systems
2018 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS)
Year: 2018 | Conference Paper |
Incompleteness and dynamic changes of the system model cause more difficulty for the model based discrete event system diagnosis. Evolving the system model to the complete and true model in running time is one of the effective ways to solve this problem. Based on the actual observed events sequence obtained in system running time?a system model evolution algorithm is proposed according to the maxi...Show More
Dynamic modeling of robot based on neural network with incomplete state observations
2017 IEEE International Conference on Robotics and Biomimetics (ROBIO)
Year: 2017 | Conference Paper |
Cited by: Papers (2)
This paper presents a novel dynamic modeling method of robot system using a recurrent neural network (RNN) with incomplete state variables observation. A dynamic model of a 2-DOF articulated robot is discussed, and the corresponding training method is deduced based on the back propagation through time (BPTT) algorithm. The effectiveness of this process is verified by simulation. The results show t...Show More
Granular Fuzzy Rule-Based Modeling With Incomplete Data Representation
IEEE Transactions on Cybernetics
Cited by: Papers (29)
Incomplete data are frequently encountered and bring difficulties when it comes to further processing. The concepts of granular computing (GrC) help deliver a higher level of abstraction to address this problem. Most of the existing data imputation and related modeling methods are of numeric nature and require prior numeric models to be provided. The underlying objective of this study is to introd...Show More
Identifiability of Two-Tree Mixtures for Group-Based Models
Elizabeth S. Allman;Sonia Petrović;John A. Rhodes;Seth Sullivant
Phylogenetic data arising on two possibly different tree topologies might be mixed through several biological mechanisms, including incomplete lineage sorting or horizontal gene transfer in the case of different topologies, or simply different substitution processes on characters in the case of the same topology. Recent work on a 2-state symmetric model of character change showed that for 4 taxa, ...Show More
Learning Spatiotemporal Graphical Models From Incomplete Observations
Amirhossein Javaheri;Arash Amini;Farokh Marvasti;Daniel P. Palomar
This paper investigates the problem of learning a graphical model from incomplete spatio-temporal measurements. Our purpose is to analyze a time-varying graph signal represented by an incomplete data matrix, the rows and columns of which correspond to spatial and temporal features/measurements of the signal, respectively. In contrast to the conventional approaches which utilize either a directed o...Show More
Analysis on Betrand model of oligopoly market
2010 2nd IEEE International Conference on Information and Financial Engineering
Year: 2010 | Conference Paper |
Cited by: Papers (1)
To analyse the pricing strategy of oligopoly market more accurately and more perfectly, analysis on Betrand model of complete information and dynamic decision, Betrand complete information model of multi-oligopolies market, and Betrand incomplete information model of multi-oligopolies market are put forward through analysing the domestic oligopoly market and researching traditional Bertrand model ...Show More
Maximum likelihood parameter determination method for complex system modeling
2011 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering
Year: 2011 | Conference Paper |
Models are often used to characterize a complex system in analysis problems. In modeling process, it is very difficult that model parameters are determined. So, a maximum likelihood parameter determination method for complex system modeling is presented to solve this problem. Therefore, a merge method is adapted to achieve the model parameter for complex systems. The presented method is a kind of ...Show More
Achieving completeness in the verification of action theories by Bounded Model Checking in ASP
Temporal logics are well suited for reasoning about actions, as they allow for the specification of domain descriptions including temporal constraints as well as for the verification of temporal properties. The article deals with verification of action theories defined in a temporal extension of answer set programming which combines ASP with a dynamic linear time temporal logic (DLTL). The article...Show More
LIHAN: A Lattice-Guided Incomplete Heterogeneous Information Network Embedding Model for Node Classification
Real-world heterogeneous information networks (HINs) are modeled as heterogeneous graphs, in which features and structures are often incomplete. Existing models employ manual imputation or dynamic adjustment to populate the incomplete data. However, there are some limitations in incomplete heterogeneous graph representation learning: 1) using populated data may lose content and high-level interact...Show More
Research on strategies to improve model accuracy based on incomplete time series data
2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)
Year: 2021 | Conference Paper |
Aiming at the problem of inaccurate model prediction caused by incomplete time series data, an improved denoising autoencoder was proposed to supplement missing data. Firstly, the convolutional neural network is added into the encoding and decoding of the denoising autoencoder for sequence analysis. The missing values are completed by making full use of the spatial correlation in the time series a...Show More
Epidural Stimulation of Rat Spinal Cord at Lumbosacral Segment Using a Surface Electrode: A Computer Simulation Study
Clinical research indicates that the epidural spinal cord stimulation (ESCS) at lumbosacral segment has shown potential for promoting locomotor recovery in patients with incomplete spinal cord injury. However, the underlying neural mechanism needs to be determined by animal experiments. In order to refine experimental protocols, we used a finite element simulation to investigate the activation of ...Show More
Energy Consumption Prediction of a CNC Machining Process With Incomplete Data
Energy consumption prediction of a CNC machining process is important for energy efficiency optimization strategies. To improve the generalization abilities, more and more parameters are acquired for energy prediction modeling. While the data collected from workshops may be incomplete because of misoperation, unstable network connections, and frequent transfers, etc. This work proposes a framework...Show More
Pricing and Empirical Analysis of Chinese Defaultable Corporate Bonds
2013 Sixth International Conference on Business Intelligence and Financial Engineering
Year: 2013 | Conference Paper |
The default of Suntech Power make the year 2013 become "the first year of default" of Chinese bond market. People are also clearly aware of the risk of default on bonds again, which shows that fair pricing for default able corporate bonds is very important. This paper prices for the "11 super JGBS" based on Merton Model, reduced model and incomplete information model and also compares with the act...Show More
Calibrated Si Mobility and Incomplete Ionization Models with Field Dependent Ionization Energy for Cryogenic Simulations
2020 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD)
Year: 2020 | Conference Paper |
Cited by: Papers (21)
Cryogenic silicon CMOS operating between 77K and 4.2K is becoming more popular in high-speed server applications and the periphery of quantum computers. In the cryogenic regime, dopant incomplete ionization and field enhanced ionization become dominating physical phenomena. Therefore, it is important to use accurate and well-calibrated mobility and incomplete ionization models in cryogenic TCAD si...Show More
iArchiMate: A Tool for Managing Imperfection in Enterprise Models
2014 IEEE 18th International Enterprise Distributed Object Computing Conference Workshops and Demonstrations
Year: 2014 | Conference Paper |
Cited by: Papers (9)
Enterprise Architecture (EA) models are built for representing one enterprise under study and are used for performing analyses that support decision making processes. EA models are created based on information provided by different kinds of sources, but these sources could be insufficient or the information could be incomplete or incorrect regarding aspects of the enterprise. As a result, modelers...Show More
A Comparison of the Effects of Data Imputation Methods on Model Performance
Wooyoung Kim;Wonwoong Cho;Jangho Choi;Jiyong Kim;Cheonbok Park;Jaegul Choo
2019 21st International Conference on Advanced Communication Technology (ICACT)
Year: 2019 | Conference Paper |
Cited by: Papers (10)
Missing values cause critical problems on training a prediction model. Various missing data imputation methods have been introduced to settle down the problem. However, the imputation accuracy obtained by the methods is insufficient to validate performance of prediction models. Thus, in this study, we compare (1) imputation accuracy from various imputation methods as well as (2) the effects of imp...Show More
Testing and debugging UML models based on fUML
2012 34th International Conference on Software Engineering (ICSE)
Year: 2012 | Conference Paper |
Cited by: Papers (7)
Model-driven development, which has recently gained momentum in academia as well as in industry, changed the software engineering process significantly from being code-centric to being model-centric. Models are considered as the key artifacts and as a result the success of the whole software development process relies on these models and their quality. Consequently, there is an urgent need for ade...Show More
Incomplete Label Multiple Instance Multiple Label Learning
With increasing data volumes, the bottleneck in obtaining data for training a given learning task is the cost of manually labeling instances within the data. To alleviate this issue, various reduced label settings have been considered including semi-supervised learning, partial- or incomplete-label learning, multiple-instance learning, and active learning. Here, we focus on multiple-instance multi...Show More
Scalable Low Dimensional Manifold Model In The Reconstruction Of Noisy And Incomplete Hyperspectral Images
2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
We present a scalable low dimensional manifold model for the reconstruction of noisy and incomplete hyperspectral images. The model is based on the observation that the spatial-spectral blocks of a hyperspectral image typically lie close to a collection of low dimensional manifolds. To emphasize this, the dimension of the manifold is directly used as a regularizer in a variational functional, whic...Show More
The advanced productivity of the modern society has created a wide range of similar commodities. However, the descriptions of commodities are always incomplete. Therefore, it is difficult for consumers to make choices. In the face of this problem, skyline query is a useful tool. However, the existing algorithms are unable to address incomplete probabilistic databases. In addition, it is necessary ...Show More
Half-Quadratic Minimization for Unsupervised Feature Selection on Incomplete Data
Unsupervised feature selection (UFS) is a popular technique of reducing the dimensions of high-dimensional data. Previous UFS methods were often designed with the assumption that the whole information in the data set is observed. However, incomplete data sets that contain unobserved information can be often found in real applications, especially in industry. Thus, these existing UFS methods have a...Show More
Simplifying Software Metric Models via Hierarchical LASSO with Incomplete Data Samples
2010 Second World Congress on Software Engineering
Year: 2010 | Conference Paper |
Cited by: Papers (1)
Software metric models can be used in predicting the interested target software metric(s) for future software project based on certain related metric(s). However, during the construction of such a model, incomplete data often appear in data sample gained from analogous past projects. In addition, whether a particular continuous predictor metric or a particular category for a certain categorical pr...Show More
Three-Dimensional Information Modeling Based on Incomplete Data for Anchor Engineering
Zhang Songan;Hu Wei;Zeng Liang;Yang Junjie;Zhong Yusen;Liu Zhen;Zhou Cuiying
Intelligent design is a trend in geotechnical engineering. However, a middle link between demand identification and intelligent design is missing in anchor engineering, resulting in lower efficiency in terms of the interaction between the anchor components, geological body and modeling information. To address this challenge, this paper proposes a method for modeling a three-dimensional anchor engi...Show More
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