scholarly journals A Bayesian Approach for an Efficient Data Reduction in IoT

2018 ◽  
pp. 3-10 ◽  
Author(s):  
Cristanel Razafimandimby ◽  
Valeria Loscrí ◽  
Anna Maria Vegni ◽  
Driss Aourir ◽  
Alessandro Neri
Keyword(s):  
Bayesian Approach ◽  
Data Reduction ◽  
Efficient Data

scholarly journals An efficient data reduction method and its application to cluster analysis

2017 ◽  
Vol 238 ◽  
pp. 234-244 ◽  
Author(s):  
Jianpei Wang ◽  
Shihong Yue ◽  
Xiao Yu ◽  
Yaru Wang
Keyword(s):  
Cluster Analysis ◽  
Data Reduction ◽  
Reduction Method ◽  
Efficient Data ◽  
Data Reduction Method

scholarly journals Efficient data reduction for large-scale genetic mapping

2015 ◽  
Author(s):  
Veronika Strnadová-Neeley ◽  
Aydın Buluç ◽  
Jarrod Chapman ◽  
John R. Gilbert ◽  
Joseph Gonzalez ◽  
...  
Keyword(s):  
Genetic Mapping ◽  
Data Reduction ◽  
Large Scale ◽  
Efficient Data

Efficient data reduction in multimedia data

2006 ◽  
Vol 25 (3) ◽  
pp. 359-374 ◽  
Author(s):  
Surong Wang ◽  
Manoranjan Dash ◽  
Liang-Tien Chia ◽  
Min Xu
Keyword(s):  
Data Reduction ◽  
Multimedia Data ◽  
Efficient Data

scholarly journals Using demographics toward efficient data classification in citizen science: a Bayesian approach

2019 ◽  
Vol 5 ◽  
pp. e239
Author(s):  
Pietro De Lellis ◽  
Shinnosuke Nakayama ◽  
Maurizio Porfiri
Keyword(s):  
Bayesian Inference ◽  
Citizen Science ◽  
Bayesian Approach ◽  
Prior Information ◽  
Data Accuracy ◽  
Majority Voting ◽  
Accurate Information ◽  
Training Set ◽  
Level Of Education ◽  
Efficient Data

Public participation in scientific activities, often called citizen science, offers a possibility to collect and analyze an unprecedentedly large amount of data. However, diversity of volunteers poses a challenge to obtain accurate information when these data are aggregated. To overcome this problem, we propose a classification algorithm using Bayesian inference that harnesses diversity of volunteers to improve data accuracy. In the algorithm, each volunteer is grouped into a distinct class based on a survey regarding either their level of education or motivation to citizen science. We obtained the behavior of each class through a training set, which was then used as a prior information to estimate performance of new volunteers. By applying this approach to an existing citizen science dataset to classify images into categories, we demonstrate improvement in data accuracy, compared to the traditional majority voting. Our algorithm offers a simple, yet powerful, way to improve data accuracy under limited effort of volunteers by predicting the behavior of a class of individuals, rather than attempting at a granular description of each of them.

scholarly journals Efficient data abstraction using weighted IB2 prototypes

2014 ◽  
Vol 11 (2) ◽  
pp. 665-678 ◽  
Author(s):  
Stefanos Ougiaroglou ◽  
Georgios Evangelidis
Keyword(s):  
Data Reduction ◽  
Training Data ◽  
Training Dataset ◽  
Data Abstraction ◽  
Prototype Selection ◽  
Initial Training ◽  
Reduction Techniques ◽  
Efficient Data ◽  
Data Reduction Technique ◽  
Better Than

Data reduction techniques improve the efficiency of k-Nearest Neighbour classification on large datasets since they accelerate the classification process and reduce storage requirements for the training data. IB2 is an effective prototype selection data reduction technique. It selects some items from the initial training dataset and uses them as representatives (prototypes). Contrary to many other techniques, IB2 is a very fast, one-pass method that builds its reduced (condensing) set in an incremental manner. New training data can update the condensing set without the need of the ?old? removed items. This paper proposes a variation of IB2, that generates new prototypes instead of selecting them. The variation is called AIB2 and attempts to improve the efficiency of IB2 by positioning the prototypes in the center of the data areas they represent. The empirical experimental study conducted in the present work as well as the Wilcoxon signed ranks test show that AIB2 performs better than IB2.

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