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Challenges in Computational Statistics and Data Mining

Overview of attention for book
Cover of 'Challenges in Computational Statistics and Data Mining'

Table of Contents

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    Book Overview
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    Chapter 1 Evolutionary Computation for Real-World Problems
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    Chapter 2 Selection of Significant Features Using Monte Carlo Feature Selection
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    Chapter 3 ADX Algorithm for Supervised Classification
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    Chapter 4 Estimation of Entropy from Subword Complexity
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    Chapter 5 Exact Rate of Convergence of Kernel-Based Classification Rule
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    Chapter 6 Compound Bipolar Queries: A Step Towards an Enhanced Human Consistency and Human Friendliness
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    Chapter 7 Process Inspection by Attributes Using Predicted Data
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    Chapter 8 Székely Regularization for Uplift Modeling
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    Chapter 9 Dominance-Based Rough Set Approach to Multiple Criteria Ranking with Sorting-Specific Preference Information
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    Chapter 10 On Things Not Seen
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    Chapter 11 Network Capacity Bound for Personalized Bipartite PageRank
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    Chapter 12 Dependence Factor as a Rule Evaluation Measure
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    Chapter 13 Recent Results on Nonparametric Quantile Estimation in a Simulation Model
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    Chapter 14 Adaptive Monte Carlo Maximum Likelihood
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    Chapter 15 What Do We Choose When We Err? Model Selection and Testing for Misspecified Logistic Regression Revisited
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    Chapter 16 Semiparametric Inference in Identification of Block-Oriented Systems
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    Chapter 17 Dealing with Data Difficulty Factors While Learning from Imbalanced Data
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    Chapter 18 Personal Privacy Protection in Time of Big Data
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    Chapter 19 Data Based Modeling
Overall attention for this book and its chapters
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About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
4 X users
googleplus
1 Google+ user

Readers on

mendeley
53 Mendeley
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Title
Challenges in Computational Statistics and Data Mining
Published by
Studies in Computational Intelligence, January 2016
DOI 10.1007/978-3-319-18781-5
ISBNs
978-3-31-918780-8, 978-3-31-918781-5
Authors

Stan Matwin, Jan Mielniczuk

Editors

Matwin, Stan, Mielniczuk, Jan

Timeline

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X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 2%
France 1 2%
Unknown 51 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 23%
Student > Master 9 17%
Student > Bachelor 6 11%
Researcher 4 8%
Student > Doctoral Student 3 6%
Other 11 21%
Unknown 8 15%
Readers by discipline Count As %
Computer Science 19 36%
Engineering 6 11%
Business, Management and Accounting 3 6%
Social Sciences 3 6%
Mathematics 2 4%
Other 9 17%
Unknown 11 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 September 2015.
All research outputs
#14,987,199
of 25,992,468 outputs
Outputs from Studies in Computational Intelligence
#1
of 1 outputs
Outputs of similar age
#196,097
of 402,374 outputs
Outputs of similar age from Studies in Computational Intelligence
#1
of 1 outputs
Altmetric has tracked 25,992,468 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1 research outputs from this source. They receive a mean Attention Score of 2.5. This one scored the same or higher as 0 of them.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 402,374 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them