Sorry, you need to enable JavaScript to visit this website.

Data Science Workshop 2018

The 2018 IEEE Data Science Workshop is a new workshop that aims to bring together researchers in academia and industry to share the most recent and exciting advances in data science theory and applications. In particular, the event will gather researchers and practitioners in various academic disciplines of data science, including signal processing, statistics, machine learning, data mining and computer science, along with experts in academic and industrial domains, such as personalized health and medicine, earth and environmental science, applied physics, finance and economics, intelligent manufacturing.

Profit Maximizing Logistic Regression Modeling for Credit Scoring


Multiple classification techniques have been employed for different business applications. In the particular case of credit scoring, a classifier which maximizes the total profit is preferable. The recently proposed expected maximum profit (EMP) measure for credit scoring allows to select the most profitable classifier. Taking the idea of the EMP one step further, it is desirable to integrate the measure into model construction, and thus obtain a profit maximizing model.

Paper Details

Authors:
Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme
Submitted On:
30 May 2018 - 8:30pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

PosterA1_Arnout_Devos_DSW2018.pdf

(13 downloads)

Keywords

Subscribe

[1] Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme, "Profit Maximizing Logistic Regression Modeling for Credit Scoring", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3221. Accessed: Jun. 20, 2018.
@article{3221-18,
url = {http://sigport.org/3221},
author = {Arnout Devos; Jakob Dhondt; Eugen Stripling; Bart Baesens; Seppe vanden Broucke; Gaurav Sukhatme },
publisher = {IEEE SigPort},
title = {Profit Maximizing Logistic Regression Modeling for Credit Scoring},
year = {2018} }
TY - EJOUR
T1 - Profit Maximizing Logistic Regression Modeling for Credit Scoring
AU - Arnout Devos; Jakob Dhondt; Eugen Stripling; Bart Baesens; Seppe vanden Broucke; Gaurav Sukhatme
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3221
ER -
Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme. (2018). Profit Maximizing Logistic Regression Modeling for Credit Scoring. IEEE SigPort. http://sigport.org/3221
Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme, 2018. Profit Maximizing Logistic Regression Modeling for Credit Scoring. Available at: http://sigport.org/3221.
Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme. (2018). "Profit Maximizing Logistic Regression Modeling for Credit Scoring." Web.
1. Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme. Profit Maximizing Logistic Regression Modeling for Credit Scoring [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3221

Learning Flexible Representations of Stochastic Processes on Graphs


Graph convolutional networks adapt the architecture of convolutional neural networks to learn rich representations of data supported on arbitrary graphs by replacing the convolution operations of convolutional neural networks with graph-dependent linear operations. However, these graph-dependent linear operations are developed for scalar functions supported on undirected graphs. We propose both a generalization of the underlying graph and a class of linear operations for stochastic (time-varying) processes on directed (or undirected) graphs to be used in graph convolutional networks.

dsw.pdf

PDF icon poster (15 downloads)

Paper Details

Authors:
Brian M. Sadler, Radu V. Balan
Submitted On:
30 May 2018 - 1:35pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster

(15 downloads)

Keywords

Subscribe

[1] Brian M. Sadler, Radu V. Balan, "Learning Flexible Representations of Stochastic Processes on Graphs", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3220. Accessed: Jun. 20, 2018.
@article{3220-18,
url = {http://sigport.org/3220},
author = {Brian M. Sadler; Radu V. Balan },
publisher = {IEEE SigPort},
title = {Learning Flexible Representations of Stochastic Processes on Graphs},
year = {2018} }
TY - EJOUR
T1 - Learning Flexible Representations of Stochastic Processes on Graphs
AU - Brian M. Sadler; Radu V. Balan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3220
ER -
Brian M. Sadler, Radu V. Balan. (2018). Learning Flexible Representations of Stochastic Processes on Graphs. IEEE SigPort. http://sigport.org/3220
Brian M. Sadler, Radu V. Balan, 2018. Learning Flexible Representations of Stochastic Processes on Graphs. Available at: http://sigport.org/3220.
Brian M. Sadler, Radu V. Balan. (2018). "Learning Flexible Representations of Stochastic Processes on Graphs." Web.
1. Brian M. Sadler, Radu V. Balan. Learning Flexible Representations of Stochastic Processes on Graphs [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3220

SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION

Paper Details

Authors:
Submitted On:
30 May 2018 - 9:30am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster2018.pdf

(15 downloads)

Keywords

Subscribe

[1] , "SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3219. Accessed: Jun. 20, 2018.
@article{3219-18,
url = {http://sigport.org/3219},
author = { },
publisher = {IEEE SigPort},
title = {SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION},
year = {2018} }
TY - EJOUR
T1 - SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3219
ER -
. (2018). SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION. IEEE SigPort. http://sigport.org/3219
, 2018. SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION. Available at: http://sigport.org/3219.
. (2018). "SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION." Web.
1. . SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3219

Uncertainty Quantification in Sunspot Counts

Paper Details

Authors:
Submitted On:
30 May 2018 - 5:31am
Short Link:
Type:
Event:
Document Year:
Cite

Document Files

poster_IEEE_2018.pdf

(15 downloads)

Keywords

Subscribe

[1] , "Uncertainty Quantification in Sunspot Counts", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3218. Accessed: Jun. 20, 2018.
@article{3218-18,
url = {http://sigport.org/3218},
author = { },
publisher = {IEEE SigPort},
title = {Uncertainty Quantification in Sunspot Counts},
year = {2018} }
TY - EJOUR
T1 - Uncertainty Quantification in Sunspot Counts
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3218
ER -
. (2018). Uncertainty Quantification in Sunspot Counts. IEEE SigPort. http://sigport.org/3218
, 2018. Uncertainty Quantification in Sunspot Counts. Available at: http://sigport.org/3218.
. (2018). "Uncertainty Quantification in Sunspot Counts." Web.
1. . Uncertainty Quantification in Sunspot Counts [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3218

Semi-Blind Inference of Topologies and Dynamical Processes over Graphs


Network science provides valuable insights across
numerous disciplines including sociology, biology, neuroscience
and engineering. A task of major practical importance in these
application domains is inferring the network structure from
noisy observations at a subset of nodes. Available methods for
topology inference typically assume that the process over the
network is observed at all nodes. However, application-specific
constraints may prevent acquiring network-wide observations.

Paper Details

Authors:
Vassilis N. Ioannidis, Yanning Shen, Georgios B. Giannakis
Submitted On:
29 May 2018 - 1:31pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

dsw_viysgg_18.pdf

(17 downloads)

Keywords

Subscribe

[1] Vassilis N. Ioannidis, Yanning Shen, Georgios B. Giannakis, "Semi-Blind Inference of Topologies and Dynamical Processes over Graphs", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3214. Accessed: Jun. 20, 2018.
@article{3214-18,
url = {http://sigport.org/3214},
author = {Vassilis N. Ioannidis; Yanning Shen; Georgios B. Giannakis },
publisher = {IEEE SigPort},
title = {Semi-Blind Inference of Topologies and Dynamical Processes over Graphs},
year = {2018} }
TY - EJOUR
T1 - Semi-Blind Inference of Topologies and Dynamical Processes over Graphs
AU - Vassilis N. Ioannidis; Yanning Shen; Georgios B. Giannakis
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3214
ER -
Vassilis N. Ioannidis, Yanning Shen, Georgios B. Giannakis. (2018). Semi-Blind Inference of Topologies and Dynamical Processes over Graphs. IEEE SigPort. http://sigport.org/3214
Vassilis N. Ioannidis, Yanning Shen, Georgios B. Giannakis, 2018. Semi-Blind Inference of Topologies and Dynamical Processes over Graphs. Available at: http://sigport.org/3214.
Vassilis N. Ioannidis, Yanning Shen, Georgios B. Giannakis. (2018). "Semi-Blind Inference of Topologies and Dynamical Processes over Graphs." Web.
1. Vassilis N. Ioannidis, Yanning Shen, Georgios B. Giannakis. Semi-Blind Inference of Topologies and Dynamical Processes over Graphs [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3214

False Discovery Rate Control with Concave Penalties using Stability Selection


False discovery rate (FDR) control is highly desirable in several high-dimensional estimation problems. While solving such problems, it is observed that traditional approaches such as the Lasso select a high number of false positives, which increase with higher noise and correlation levels in the dataset. Stability selection is a procedure which uses randomization with the Lasso to reduce the number of false positives.

Paper Details

Authors:
Kush R. Varshney
Submitted On:
29 May 2018 - 1:22pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

Poster for FDR Control with Concave Penalties using Stability Selection

(18 downloads)

Keywords

Subscribe

[1] Kush R. Varshney, "False Discovery Rate Control with Concave Penalties using Stability Selection", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3213. Accessed: Jun. 20, 2018.
@article{3213-18,
url = {http://sigport.org/3213},
author = {Kush R. Varshney },
publisher = {IEEE SigPort},
title = {False Discovery Rate Control with Concave Penalties using Stability Selection},
year = {2018} }
TY - EJOUR
T1 - False Discovery Rate Control with Concave Penalties using Stability Selection
AU - Kush R. Varshney
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3213
ER -
Kush R. Varshney. (2018). False Discovery Rate Control with Concave Penalties using Stability Selection. IEEE SigPort. http://sigport.org/3213
Kush R. Varshney, 2018. False Discovery Rate Control with Concave Penalties using Stability Selection. Available at: http://sigport.org/3213.
Kush R. Varshney. (2018). "False Discovery Rate Control with Concave Penalties using Stability Selection." Web.
1. Kush R. Varshney. False Discovery Rate Control with Concave Penalties using Stability Selection [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3213

Non-negative Super-resolution is Stable


We consider the problem of localizing point sources on an interval from possibly noisy measurements. In the absence of noise, we show that measurements from Chebyshev sys- tems are an injective map for non-negative sparse measures, and therefore non-negativity is sufficient to ensure unique- ness for sparse measures. Moreover, we characterize non- negative solutions from inexact measurements and show that any non-negative solution consistent with the measurements is proportionally close to the solution of the system with ex- act measurements.

poster.pdf

PDF icon poster.pdf (18 downloads)

Paper Details

Authors:
Armin Eftekhari, Jared Tanner, Andrew Thompson, Bogdan Toader, Hemant Tyagi
Submitted On:
29 May 2018 - 7:34am
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

poster.pdf

(18 downloads)

Keywords

Subscribe

[1] Armin Eftekhari, Jared Tanner, Andrew Thompson, Bogdan Toader, Hemant Tyagi, "Non-negative Super-resolution is Stable", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3212. Accessed: Jun. 20, 2018.
@article{3212-18,
url = {http://sigport.org/3212},
author = {Armin Eftekhari; Jared Tanner; Andrew Thompson; Bogdan Toader; Hemant Tyagi },
publisher = {IEEE SigPort},
title = {Non-negative Super-resolution is Stable},
year = {2018} }
TY - EJOUR
T1 - Non-negative Super-resolution is Stable
AU - Armin Eftekhari; Jared Tanner; Andrew Thompson; Bogdan Toader; Hemant Tyagi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3212
ER -
Armin Eftekhari, Jared Tanner, Andrew Thompson, Bogdan Toader, Hemant Tyagi. (2018). Non-negative Super-resolution is Stable. IEEE SigPort. http://sigport.org/3212
Armin Eftekhari, Jared Tanner, Andrew Thompson, Bogdan Toader, Hemant Tyagi, 2018. Non-negative Super-resolution is Stable. Available at: http://sigport.org/3212.
Armin Eftekhari, Jared Tanner, Andrew Thompson, Bogdan Toader, Hemant Tyagi. (2018). "Non-negative Super-resolution is Stable." Web.
1. Armin Eftekhari, Jared Tanner, Andrew Thompson, Bogdan Toader, Hemant Tyagi. Non-negative Super-resolution is Stable [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3212

PREDICTING ELECTRICITY OUTAGES CAUSED BY CONVECTIVE STORMS


We consider the problem of predicting power outages in an electrical power grid due to hazards produced by convective storms. These storms produce extreme weather phenomena such as intense wind, tornadoes and lightning over a small area. In this paper, we discuss the application of state-of-the-art machine learning techniques, such as random forest classifiers and deep neural networks, to predict the amount of damage caused by storms.

Paper Details

Authors:
Joonas Karjalainen
Submitted On:
29 May 2018 - 3:22am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

SASSE_poster_IEEE_DSW_2018.pdf

(17 downloads)

Keywords

Subscribe

[1] Joonas Karjalainen, "PREDICTING ELECTRICITY OUTAGES CAUSED BY CONVECTIVE STORMS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3211. Accessed: Jun. 20, 2018.
@article{3211-18,
url = {http://sigport.org/3211},
author = {Joonas Karjalainen },
publisher = {IEEE SigPort},
title = {PREDICTING ELECTRICITY OUTAGES CAUSED BY CONVECTIVE STORMS},
year = {2018} }
TY - EJOUR
T1 - PREDICTING ELECTRICITY OUTAGES CAUSED BY CONVECTIVE STORMS
AU - Joonas Karjalainen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3211
ER -
Joonas Karjalainen. (2018). PREDICTING ELECTRICITY OUTAGES CAUSED BY CONVECTIVE STORMS. IEEE SigPort. http://sigport.org/3211
Joonas Karjalainen, 2018. PREDICTING ELECTRICITY OUTAGES CAUSED BY CONVECTIVE STORMS. Available at: http://sigport.org/3211.
Joonas Karjalainen. (2018). "PREDICTING ELECTRICITY OUTAGES CAUSED BY CONVECTIVE STORMS." Web.
1. Joonas Karjalainen. PREDICTING ELECTRICITY OUTAGES CAUSED BY CONVECTIVE STORMS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3211

Pages