BibSLEIGH — Taghi_M

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: Khoshgoftaar:Taghi_M=

Facilitated 1 volumes:

PrCh

Contributed to:

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2004

1999

1998

1996

1995

1994

1993

1989

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.