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Kanchana Viriyapant. Feature distillation of high dimension datasets: dimension contraction and component dilation. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2020.
Feature distillation of high dimension datasets: dimension contraction and component dilation
Organization :
King Mongkut's University of Technology North Bangkok. Faculty of Information Technology and Digital Innovation
Email :
jsabarivishnujayanthan@yahoo.com
Name: Kanchana Viriyapant
Organization :
King Mongkut's University of Technology North Bangkok. Faculty of Information Technology and Digital Innovation
Email :
kanchana.v@it.kmutnb.ac.th
ThaSH:
Big data
ThaSH:
Machine learning
Abstract:
Handling high dimensional datasets is always a
challenge in machine learning. The operations that deal with
datasets containing vast amounts of features are extremely timeconsuming
and computationally expensive. In an even more
challenging scenario, a data scientist may be given a dataset
without any information about the target feature and he/she
needs to find out the features that can be possibly predicted from
the dataset without loss of interpretation. To satisfy this
requirement we introduce a new technique, "Feature
Distillation". In addition to that a new algorithm, Dimension
Contraction and Component Dilation (DCCD) is proposed. The
idea is to return features that can be predicted with high
accuracies from a dataset using a combination of information
measurement, discretization, dimensionality reduction,
supervised and unsupervised techniques. The DCCD algorithm
is tested with various synthetic datasets and proven to be faster
than traditional methods.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2020
Modified:
2023-11-13
Issued:
2023-11-13
บทความ/Article
application/pdf
BibliograpyCitation :
In King Mongkut's University of Technology North Bangkok Faculty of Applied Science. The 17th International Joint Conference on Computer Science and Software Engineering (JCSSE 2020) (pp.18-23). Bangkok : King Mongkut's University of Technology North Bangkok
eng
©copyrights King Mongkut's University of Technology North Bangkok
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ใช้เวลา
0.029057 วินาที
Sabari Vishnu Jayanthan Jaikrishnan
| Title | Contributor | Type |
|---|---|---|
| Feature distillation of high dimension datasets: dimension contraction and component dilation
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Sabari Vishnu Jayanthan Jaikrishnan;Kanchana Viriyapant | บทความ/Article |
Kanchana Viriyapant
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|---|---|---|
| Scheduling parameter sweep workflow in cloud environment
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Kanchana Viriyapant | Sucha Smanchat | วิทยานิพนธ์/Thesis |
| Accommodation descriptions that influence Airbnb occupancy rate using ontology
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Rattapon Choogortoud;Dittapol Muntham;Worawek Chuethong;Sart Srisoontorn;Orasa limpaporn;Nathaporn Utakrit;Kanchana Viriyapant;Nalinpat Bhumpenpein | บทความ/Article | |
| Feature distillation of high dimension datasets: dimension contraction and component dilation
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Sabari Vishnu Jayanthan Jaikrishnan;Kanchana Viriyapant | บทความ/Article |