An Overview of Overfitting and its Solutions
By Xue Ying. Journal of Physics, Conference Series, 2019
2,159 citations recorded by the source corpus.
Overfitting is a fundamental issue in supervised machine learning which prevents us from perfectly generalizing the models to well fit observed data on training data, as well as unseen data on testing set. Because of the presence of noise, the limited size of training set, and the complexity of classifiers, overfitting happens. This paper is going to talk about overfitting from the perspectives of causes and solutions. To reduce the effects of overfitting, various strategies are proposed to address to these causes: 1) “early-stopping” strategy is introduced to prevent overfitting by stopping training before the performance stops optimize; 2) “network-reduction” strategy is used to exclude the noises in training set; 3) “data-expansion” strategy is proposed for complicated models to fine-tune the hyper-parameters sets with a great amount of data; and 4) “regularization” strategy is proposed to guarantee models performance to a great extent while dealing with real world issues by feature-selection, and by distinguishing more useful and less useful features.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/75d50a601d4830fae3a24ff55f2795ef3911924e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Thinagaran Perumal
- Norwati Mustapha
- Huandong Wang
- Tao Jiang
- Wenjie Fu
- Guanghua Liu
- Raihani Mohamed
- Yong Li
Citations in the corpus
- A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU (2023, background): “Its primary purpose is to alleviate the computational burden and address overfitting issues [18].”
- Membership Inference Attacks against Generative Models with Probabilistic Fluctuation (positive): “Besides, in the training pipeline of generative models, techniques like early stopping and regularization are widely used to prevent overfitting and improve generalization [ Ying, 2019 ] , which exacerbates the impracticality of this framework.”
- Data-centric Artificial Intelligence: A Survey (2023, neutral): “For instance, the model could overfit on noises, outliers, and irrelevant extracted features, resulting in reduced generalizability [260].”