activity
20182026
most citedA Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends

101 citations · 262 across the 38 of their papers we have counts for

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10 papers · 1 filter

cs.LG2026

Adaptive Protection for Evolutionary Feature Construction in Symbolic Regression with Application to Credit Classification

Hengzhe Zhang, Qi Chen, Bing Xue +3

Evolutionary feature construction has shown strong promise in symbolic regression by automatically discovering informative transformations of input features that enhance a simple b…

cs.LG2026

EvoTSC: Evolving Feature Learning Models for Time Series Classification via Genetic Programming

Xuanhao Yang, Bing Xue, Mengjie Zhang

Time series classification is an important analytical task across diverse domains. However, its practical application is often hindered by the scarcity of labeled data and the requ…

cs.LG20261 cited

Enhancing Generalization in Evolutionary Feature Construction for Symbolic Regression through Vicinal Jensen Gap Minimization

Hengzhe Zhang, Qi Chen, Bing Xue +2

Genetic programming-based feature construction has achieved significant success in recent years as an automated machine learning technique to enhance learning performance. However,…

cs.LG2024

EvoSampling: A Granular Ball-based Evolutionary Hybrid Sampling with Knowledge Transfer for Imbalanced Learning

Wenbin Pei, Ruohao Dai, Bing Xue +4

Class imbalance would lead to biased classifiers that favor the majority class and disadvantage the minority class. Unfortunately, from a practical perspective, the minority class…

cs.LG2024

Machine Learning for Raman Spectroscopy-based Cyber-Marine Fish Biochemical Composition Analysis

Yun Zhou, Gang Chen, Bing Xue +6

The rapid and accurate detection of biochemical compositions in fish is a crucial real-world task that facilitates optimal utilization and extraction of high-value products in the…

cs.LG2024

Meta-Learning Neural Procedural Biases

Christian Raymond, Qi Chen, Bing Xue +1

The goal of few-shot learning is to generalize and achieve high performance on new unseen learning tasks, where each task has only a limited number of examples available. Gradient-…