Travelled to:1 × Canada
6 × USA
Collaborated with:T.M.Khoshgoftaar K.Gao H.Wang J.V.Hulse R.Wald C.Seiffert G.Cosmai U.Cugini P.Mussio
Talks about:softwar (10) data (8) select (7) metric (5) featur (5) base (5) studi (4) techniqu (3) imbalanc (3) qualiti (3)
Person: Amri Napolitano
DBLP: Napolitano:Amri
Contributed to:
20152014201320122011
2008
2007
1982Wrote 13 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-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-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.
- ICPR-2008-SeiffertKHN #classification #named #performance
- RUSBoost: Improving classification performance when training data is skewed (CS, TMK, JVH, AN), pp. 1–4.
- ICML-2007-HulseKN #learning
- Experimental perspectives on learning from imbalanced data (JVH, TMK, AN), pp. 935–942.
- DAC-1982-CosmaiCMN #2d #interactive
- An interactive drafting system based on two dimensional primitives (GC, UC, PM, AN), pp. 521–529.