User profiles for N. Chawla
 | Nitesh V Chawla, FACM, FAAAI, FIEEE, FAAASFrank Freimann Professor of CSE.,Director, Lucy Family Institute for Data & Soc, Univ. of … Verified email at nd.edu Cited by 64075 |
Data mining for imbalanced datasets: An overview
NV Chawla - Data mining and knowledge discovery handbook, 2010 - Springer
… SMOTEBoost algorithm combines SMOTE and the standard boosting procedure (Chawla
et al., 2003b). We want to utilize SMOTE for improving the accuracy over the minority classes, …
et al., 2003b). We want to utilize SMOTE for improving the accuracy over the minority classes, …
SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary
… Chawla reminisces the origins of SMOTE to a classification problem that he was tackling as
a graduate student in 2000. He was working on developing a classification algorithm to learn …
a graduate student in 2000. He was working on developing a classification algorithm to learn …
Experiential avoidance as a functional dimensional approach to psychopathology: An empirical review
N Chawla, B Ostafin - Journal of clinical psychology, 2007 - Wiley Online Library
The construct of experiential avoidance has become more frequently used by clinical
researchers. Experiential avoidance involves the unwillingness to remain in contact with private …
researchers. Experiential avoidance involves the unwillingness to remain in contact with private …
SMOTE: synthetic minority over-sampling technique
… For SMOTE-N we can ignore these weights in equation 2, as SMOTE-N is not used for
classification purposes directly. However, we can redefine these weights to give more weight to …
classification purposes directly. However, we can redefine these weights to give more weight to …
Special issue on learning from imbalanced data sets
NV Chawla, N Japkowicz, A Kotcz - ACM SIGKDD explorations …, 2004 - dl.acm.org
The class imbalance problem is one of the (relatively) new problems that emerged when
machine learning matured from an embryonic science to an applied technology, amply used in …
machine learning matured from an embryonic science to an applied technology, amply used in …
SMOTEBoost: Improving prediction of the minority class in boosting
… We applied SMOTE with different values for the parameter N that specifies the amount of …
class examples, and increasing the SMOTE parameter N to values larger than 200 causes the …
class examples, and increasing the SMOTE parameter N to values larger than 200 causes the …
metapath2vec: Scalable representation learning for heterogeneous networks
We study the problem of representation learning in heterogeneous networks. Its unique
challenges come from the existence of multiple types of nodes and links, which limit the …
challenges come from the existence of multiple types of nodes and links, which limit the …
[HTML][HTML] Nivolumab plus ipilimumab with or without live bacterial supplementation in metastatic renal cell carcinoma: a randomized phase 1 trial
…, Z Zengin, N Salgia, S Salgia, J Malhotra, N Chawla… - Nature medicine, 2022 - nature.com
… using n = 52 stool samples from n = 26 patients (n = 18 patients in the nivolumab–ipilimumab
with CBM588 arm (n = 11 responders and n = 7 non-responders); and n = 8 patients (…
with CBM588 arm (n = 11 responders and n = 7 non-responders); and n = 8 patients (…
Heterogeneous graph neural network
Representation learning in heterogeneous graphs aims to pursue a meaningful vector
representation for each node so as to facilitate downstream applications such as link prediction, …
representation for each node so as to facilitate downstream applications such as link prediction, …
SVMs modeling for highly imbalanced classification
Traditional classification algorithms can be limited in their performance on highly unbalanced
data sets. A popular stream of work for countering the problem of class imbalance has …
data sets. A popular stream of work for countering the problem of class imbalance has …