Journal ArticleDOI
Toward semantic data imputation for a dengue dataset
TLDR
An improvement in the efficiency of predicting missing data utilizing Particle Swarm Optimization (PSO), which is applied to the numerical data cleansing problem, with the performance of PSO being enhanced using K-means to help determine the fitness value.Abstract:
Missing data are a major problem that affects data analysis techniques for forecasting. Traditional methods suffer from poor performance in predicting missing values using simple techniques, e.g., mean and mode. In this paper, we present and discuss a novel method of imputing missing values semantically with the use of an ontology model. We make three new contributions to the field: first, an improvement in the efficiency of predicting missing data utilizing Particle Swarm Optimization (PSO), which is applied to the numerical data cleansing problem, with the performance of PSO being enhanced using K-means to help determine the fitness value. Second, the incorporation of an ontology with PSO for the purpose of narrowing the search space, to make PSO provide greater accuracy in predicting numerical missing values while quickly converging on the answer. Third, the facilitation of a framework to substitute nominal data that are lost from the dataset using the relationships of concepts and a reasoning mechanism concerning the knowledge-based model. The experimental results indicated that the proposed method could estimate missing data more efficiently and with less chance of error than conventional methods, as measured by the root mean square error.read more
Citations
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Journal ArticleDOI
A Critical Review of Real-Time Modelling of Flood Forecasting in Urban Drainage Systems
TL;DR: In this article , the authors present a comprehensive review of the current state-of-the-art and future trends of real-time modelling of flood forecasting in urban drainage systems.
Journal ArticleDOI
Semantic data mining in the information age: A systematic review
TL;DR: A comprehensive overview of the literature on domain ontologies as used in the various semantic data‐mining tasks, such as preprocessing, modeling, and postprocessing is provided.
Journal ArticleDOI
Virtual sensor-based imputed graph attention network for anomaly detection of equipment with incomplete data
TL;DR: Wang et al. as discussed by the authors proposed a virtual sensor-based imputed graph attention network, which generates signals to impute the time of sensor record failure by generative adversarial network (GAN) and extracts the features of complete signals mixed with real signals and generated signals by GAT.
Posted Content
Nearest Neighbor Imputation for Categorical Data by Weighting of Attributes
Shahla Faisal,Gerhard Tutz +1 more
TL;DR: The weighted nearest neighbors approach is extended to impute missing values in categorical variables and shows that the weighting of attributes yields smaller imputation errors than existing approaches.
Journal ArticleDOI
Intelligent approach to automated star-schema construction using a knowledge base
TL;DR: A new strategy that incorporates knowledge-based models into a framework, named the Semantic-based Star-schema Designer, that assists the automation of star schema construction and their relationship information without human intervention using homegrown algorithms.
References
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Proceedings ArticleDOI
Ontology Based Web Page Classification System by Using Enhanced C4.5 and Naïve Bayesian Classifiers
TL;DR: This system proposes as the web page classification system based on semantic logic, which uses the enhanced C4.5 decision tree and Naive Bayesian (NB) classifiers for classification.
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