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Stop Oversampling for Class Imbalance Learning: A Critical Review
Given data and methods in hand, we argue that oversampling in its current forms and methodologies is unreliable for learning from class imbalanced data and should be avoided in real-world applications.
Fuzzy Win-Win: A Novel Approach to Quantify Win-Win Using Fuzzy Logic
The classic win-win has a key flaw in that it cannot offer the parties the right amounts of winning because each party believes they are winners.
Deep learning for identification and face, gender, expression recognition under constraints
The main objective of this work is to test the ability of deep learning based automated computer system to identify not only persons, but also to perform recognition of gender, age, and facial expressions such as eye smile.
Detailed Investigation of Deep Features with Sparse Representation and Dimensionality Reduction in CBIR: A Comparative Study
Research on content-based image retrieval (CBIR) has been under development for decades, and numerous methods have been competing to extract the most discriminative features for improved representation of the image content.
Greedy Algorithms for Approximating the Diameter of Machine Learning Datasets in Multidimensional Euclidean Space
Finding the diameter of a dataset in multidimensional Euclidean space is a well-established problem, with well-known algorithms.
