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Travelled to:
1 × Brazil
1 × France
1 × The Netherlands
12 × USA
4 × Canada
Collaborated with:
A.Napolitano K.Gao H.Wang J.V.Hulse E.B.Allen J.C.Munson N.Seliya J.C.Sloan C.Seiffert R.M.Szabo D.L.Lanning W.D.Jones A.Folleco R.Wald V.H.Joshi J.P.Hudepohl P.Lin A.Varas L.A.Bullard D.J.Drown Y.Xiao J.M.Voas X.Su X.Zhu R.Greiner R.Halstead G.P.Trio X.Yuan T.T.Pearse G.E.Stark
Talks about:
softwar (27) data (13) qualiti (12) select (10) model (10) predict (7) featur (7) studi (7) metric (6) base (6)

♂ Person: Taghi M. Khoshgoftaar

DBLP DBLP: Khoshgoftaar:Taghi_M=

Facilitated 1 volumes:

ICSM 1998PrCh

Contributed to:

SEKE 20152015
SEKE 20142014
SEKE 20132013
SEKE 20122012
SEKE 20112011
SEKE 20102010
SEKE 20092009
ICPR 20082008
SAC 20082008
SEKE 20082008
ICML 20072007
SEKE 20072007
SEKE 20062006
SEKE 20042004
CSMR 19991999
ICSM 19991999
ICSM 19981998
ICSM 19961996
ICSM 19951995
ICSM 19941994
CSM 19931993
ICSE 19891989

Wrote 38 papers:

SEKE-2015-GaoKN #set
Combining Feature Subset Selection and Data Sampling for Coping with Highly Imbalanced Software Data (KG, TMK, AN), pp. 439–444.
SEKE-2015-WangKN #feature model #re-engineering
Stability of Three Forms of Feature Selection Methods on Software Engineering Data (HW, TMK, AN), pp. 385–390.
SEKE-2014-GaoKN #estimation #learning #quality #ranking
Comparing Two Approaches for Adding Feature Ranking to Sampled Ensemble Learning for Software Quality Estimation (KG, TMK, AN), pp. 280–285.
SEKE-2014-WangKN #classification #fault #metric #performance #predict
Choosing the Best Classification Performance Metric for Wrapper-based Software Metric Selection for Defect Prediction (HW, TMK, AN), pp. 540–545.
SEKE-2013-GaoKN #estimation #preprocessor #quality
Exploring Ensemble-Based Data Preprocessing Techniques for Software Quality Estimation (KG, TMK, AN), pp. 612–617.
SEKE-2013-Khoshgoftaar #big data #challenge
Overcoming Big Data Challenges (TMK).
SEKE-2013-WangKWN #case study #feature model #first-order #metric #statistics
A Study on First Order Statistics-Based Feature Selection Techniques on Software Metric Data (HW, TMK, RW, AN), pp. 467–472.
SEKE-2012-GaoKN #feature model #metric
Stability of Filter-Based Feature Selection Methods for Imbalanced Software Measurement Data (KG, TMK, AN), pp. 74–79.
SEKE-2012-WangKWN #empirical #fault #metric #predict
An Empirical Study of Software Metric Selection Techniques for Defect Prediction (HW, TMK, RW, AN), pp. 94–99.
SEKE-2011-GaoK #fault #predict
Software Defect Prediction for High-Dimensional and Class-Imbalanced Data (KG, TMK), pp. 89–94.
SEKE-2011-KhoshgoftaarGN #case study #comparative #predict #quality
A Comparative Study of Different Strategies for Predicting Software Quality (TMK, KG, AN), pp. 65–70.
SEKE-2011-WangKN #empirical #metric #using
An Empirical Study of Software Metrics Selection Using Support Vector Machine (HW, TMK, AN), pp. 83–88.
SEKE-2010-KhoshgoftaarG #machine learning #metric #novel #re-engineering #using
Software Engineering with Computational Intelligence and Machine Learning A Novel Software Metric Selection Technique Using the Area Under ROC Curves (TMK, KG), pp. 203–208.
SEKE-2010-WangKG #classification #feature model #quality
Ensemble Feature Selection Technique for Software Quality Classification (HW, TMK, KG), pp. 215–220.
SEKE-2009-LinWK #algorithm #feature model #hybrid #novel
A Novel Hybrid Search Algorithm for Feature Selection (PL, HW, TMK), pp. 81–86.
SEKE-2009-SeliyaK #modelling #quality
Value-Based Software Quality Modeling (NS, TMK), pp. 116–121.
SEKE-2009-SloanKV
An Extendible Translation of BPEL to a Machine-verifiable Model (JCS, TMK, AV), pp. 344–349.
ICPR-2008-SeiffertKHN #classification #named #performance
RUSBoost: Improving classification performance when training data is skewed (CS, TMK, JVH, AN), pp. 1–4.
SAC-2008-SuKZG #classification #collaboration #machine learning #using
Imputation-boosted collaborative filtering using machine learning classifiers (XS, TMK, XZ, RG), pp. 949–950.
SEKE-2008-FollecoKB #classification #quality
Analyzing the Impact of Attribute Noise on Software Quality Classification (AF, TMK, LAB), pp. 73–78.
SEKE-2008-KhoshgoftaarSD #knowledge-based #modelling #on the #quality
On the Rarity of Fault-prone Modules in Knowledge-based Software Quality Modeling (TMK, NS, DJD), pp. 279–284.
SEKE-2008-SloanK #model checking #towards #web #web service
Toward Model Checking Web Services Over the Web (JCS, TMK), pp. 519–524.
ICML-2007-HulseKN #learning
Experimental perspectives on learning from imbalanced data (JVH, TMK, AN), pp. 935–942.
SEKE-2007-FollecoKHS #learning #quality
Learning from Software Quality Data with Class Imbalance and Noise (AF, TMK, JVH, CS), p. 487–?.
SEKE-2006-KhoshgoftaarH #case study #metric #multi
Multiple Imputation of Software Measurement Data: A Case Study (TMK, JVH), pp. 220–226.
SEKE-2006-KhoshgoftaarSH #metric
Polishing Noise in Continuous Software Measurement Data (TMK, CS, JVH), pp. 227–231.
SEKE-2004-KhoshgoftaarJ #case study #classification #quality
Noise Elimination with Ensemble-Classifier Filtering: A Case-Study in Software Quality Engineerin (TMK, VHJ), pp. 226–231.
SEKE-2004-KhoshgoftaarXG #modelling #multi #optimisation
Multi-Objective Optimization by CBR GA-Optimizer for Module-Order Modeling (TMK, YX, KG), pp. 220–225.
CSMR-1999-JonesHKA #modelling #quality
Application of a Usage Profile in Software Quality Models (WDJ, JPH, TMK, EBA), pp. 148–159.
ICSM-1999-KhoshgoftaarAYJH #experience #fault #legacy #metric #predict
Experience Paper: Preparing Measurements of Legacy Software for Predicting Operational Faults (TMK, EBA, XY, WDJ, JPH), p. 359–?.
ICSM-1998-JonesKMPS #evolution #modelling #quality
Hitting the Moving Target: Trials and Tribulations of Modeling Quality in Evolving Software Systems (WDJ, TMK, JCM, TTP, GES), pp. 66–67.
ICSM-1998-KhoshgoftaarA #quality #question
Can a Software Quality Model Hit a Moving Target? (TMK, EBA), pp. 68–70.
ICSM-1996-KhoshgoftaarAHT #detection #lifecycle
Detection of Fault-Prone Software Modules During a Spiral Life Cycle (TMK, EBA, RH, GPT), pp. 69–76.
ICSM-1995-KhoshgoftaarSV #detection #testing
Detecting program modules with low testability (TMK, RMS, JMV), pp. 242–250.
ICSM-1994-KhoshgoftaarS #maintenance #predict
Improving Code Churn Predictions During the System Test and Maintenance Phases (TMK, RMS), pp. 58–67.
ICSM-1994-LanningK #canonical #complexity #fault #modelling #process
Canonical Modeling of Software Complexity and Fault Correction Activity (DLL, TMK), pp. 374–381.
CSM-1993-KhoshgoftaarML #case study #comparative #maintenance #modelling #predict #testing
A Comparative Study of Predictive Models for Program Changes During System Testing and Maintenance (TMK, JCM, DLL), pp. 72–79.
ICSE-1989-MunsonK #complexity
The Dimensionality of Program Complexity (JCM, TMK), pp. 245–253.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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