508 citations · 585 across the 4 of their papers we have counts for
7 papers
Kernel k-Means, By All Means: Algorithms and Strong Consistency
Debolina Paul, Saptarshi Chakraborty, Swagatam Das +1
Kernel -means clustering is a powerful tool for unsupervised learning of non-linearly separable data. Since the earliest attempts, researchers have noted that such algorithms of…
Appropriateness of Performance Indices for Imbalanced Data Classification: An Analysis
Sankha Subhra Mullick, Shounak Datta, Sourish Gunesh Dhekane +1
Indices quantifying the performance of classifiers under class-imbalance, often suffer from distortions depending on the constitution of the test set or the class-specific classifi…
Recent Trends in the Use of Statistical Tests for Comparing Swarm and Evolutionary Computing Algorithms: Practical Guidelines and a Critical Review
J. Carrasco, S. García, M. M. Rueda +2
A key aspect of the design of evolutionary and swarm intelligence algorithms is studying their performance. Statistical comparisons are also a crucial part which allows for reliabl…
Utilizing Dependence among Variables in Evolutionary Algorithms for Mixed-Integer Programming: A Case Study on Multi-Objective Constrained Portfolio Optimization
Yi Chen, Aimin Zhou, Swagatam Das
Several real-world applications could be modeled as Mixed-Integer Non-Linear Programming (MINLP) problems, and some prominent examples include portfolio optimization, remote sensin…
A Strongly Consistent Sparse -means Clustering with Direct Penalization on Variable Weights
Saptarshi Chakraborty, Swagatam Das
We propose the Lasso Weighted -means (--means) algorithm as a simple yet efficient sparse clustering procedure for high-dimensional data where the number of features (…
Generative Adversarial Minority Oversampling
Sankha Subhra Mullick, Shounak Datta, Swagatam Das
Class imbalance is a long-standing problem relevant to a number of real-world applications of deep learning. Oversampling techniques, which are effective for handling class imbalan…