NDLI: VisGBT: Visually analyzing evolving datasets for adaptive learning

Content Provider	IEEE Xplore Digital Library
Author	Keke Chen Fengguang Tian
Copyright Year	2009
Description	Author affiliation: Department of Computer Science and Engineering, Wright State University, Dayton, OH 45435, USA (Keke Chen; Fengguang Tian)
Abstract	Many machine learning problems involve changes in both feature distribution and label distribution, such as domain adaptation and learning drifting concepts from data streams. Correctly detecting, identifying, and understanding the changes of data distributions can help us properly select data samples or algorithms for learning models. However, since the training datasets are often in high dimensionality and large size, it has been difficult to effectively analyze them. Furthermore, the joint distribution between features and labels makes the problem more difficult to handle. In this paper, we propose a visual analysis method (VisGBT) that combines the gradient-boosting-trees (GBT) modeling method, regression analysis, and multidimensional visualization to capture the mismatches between datasets and models. The GBT model consists of a series of trees with a predefined number of terminal (leaf) nodes per tree. These terminal nodes partition the high dimensional space with a few most informative features to minimize the label prediction error. VisGBT maps various kinds of detailed model information to the terminal node matrix (TNM) and visualizes it with an appropriate design. With this visual analysis method, we can easily find out the detailed differences between datasets with the help of a learned model. We will illustrate the use of various visual patterns and in particular show how this method can help us analyze domain similarity for domain adaptation.
Starting Page	1
Ending Page	10
File Size	10256836
Page Count	10
File Format	PDF
ISBN	9789639799769
DOI	10.4108/ICST.COLLABORATECOM2009.8281
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	ICST
Subject Keyword	Computer science Data analysis Multidimensional systems Costs Machine learning algorithms Data visualization Training data Machine learning Data engineering Regression analysis
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

RadViz and Identification of Clusters in Multidimensional Data

Using projections to visually cluster high-dimensional data

Evolving artificial datasets to improve interpretable classifiers

Greedy Algorithms for Approximating the Diameter of Machine Learning Datasets in Multidimensional Euclidean Space

Evolving materialized views in data warehouse

QUBO Formulations for Training Machine Learning Models

High Performance Dimension Reduction and Visualization for Large High-Dimensional Data Analysis

A Machine Learning Approach for Analyzing Musical Expressions of Piano Performance

Selecting Machine Learning Algorithms Using Regression Models

VisGBT: Visually analyzing evolving datasets for adaptive learning

Similar Documents

RadViz and Identification of Clusters in Multidimensional Data

Using projections to visually cluster high-dimensional data

Evolving artificial datasets to improve interpretable classifiers

Greedy Algorithms for Approximating the Diameter of Machine Learning Datasets in Multidimensional Euclidean Space

Evolving materialized views in data warehouse

QUBO Formulations for Training Machine Learning Models

High Performance Dimension Reduction and Visualization for Large High-Dimensional Data Analysis

A Machine Learning Approach for Analyzing Musical Expressions of Piano Performance

Selecting Machine Learning Algorithms Using Regression Models

VisGBT: Visually analyzing evolving datasets for adaptive learning