Journal ArticleDOI
Pattern classification with missing data: a review
TLDR
The aim of this work is to analyze the missing data problem in pattern classification tasks, and to summarize and compare some of the well-known methods used for handling missing values.Abstract:
Pattern classification has been successfully applied in many problem domains, such as biometric recognition, document classification or medical diagnosis. Missing or unknown data are a common drawback that pattern recognition techniques need to deal with when solving real-life classification tasks. Machine learning approaches and methods imported from statistical learning theory have been most intensively studied and used in this subject. The aim of this work is to analyze the missing data problem in pattern classification tasks, and to summarize and compare some of the well-known methods used for handling missing values.read more
Citations
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Journal ArticleDOI
Multiple Imputation for Nonresponse in Surveys
TL;DR: It is concluded that multiple Imputation for Nonresponse in Surveys should be considered as a legitimate method for answering the question of why people do not respond to survey questions.
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Flexible Imputation of Missing Data
TL;DR: The problem of missing data concepts of MCAR, MAR and MNAR simple solutions that do not (always) work multiple imputation in a nutshell and some dangers, some do's and some don'ts are covered.
Journal ArticleDOI
Recurrent Neural Networks for Multivariate Time Series with Missing Values.
TL;DR: In this article, a deep learning model based on Gated Recurrent Unit (GRU) is proposed to exploit the missing values and their missing patterns for effective imputation and improving prediction performance.
Proceedings ArticleDOI
Health Monitoring and Management Using Internet-of-Things (IoT) Sensing with Cloud-Based Processing: Opportunities and Challenges
Moeen Hassanalieragh,Alex Page,Tolga Soyata,Gaurav Sharma,Mehmet K. Aktas,Gonzalo Mateos,Burak Kantarci,Silvana Andreescu +7 more
TL;DR: The availability of data at hitherto unimagined scales and temporal longitudes coupled with a new generation of intelligent processing algorithms can facilitate an evolution in the practice of medicine and help reduce the cost of health care while simultaneously improving outcomes.
Journal ArticleDOI
Materials that couple sensing, actuation, computation, and communication
M. A. McEvoy,Nikolaus Correll +1 more
TL;DR: Robotic materials can enable smart composites that autonomously change their shape, stiffness, or physical appearance in a fully programmable way, extending the functionality of classical “smart materials.”
References
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Book
C4.5: Programs for Machine Learning
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Book
Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Book
Statistical Analysis with Missing Data
TL;DR: This work states that maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse and large-Sample Inference Based on Maximum Likelihood Estimates is likely to be high.
Journal ArticleDOI
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.