Summary of Intuitionistic Fuzzy Generalized Eigenvalue Proximal Support Vector Machine, by A. Quadir et al.
Intuitionistic Fuzzy Generalized Eigenvalue Proximal Support Vector Machine
by A. Quadir, M. A. Ganaie, M. Tanveer
First submitted to arxiv on: 3 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel intuitionistic fuzzy generalized eigenvalue proximal support vector machine (IF-GEPSVM) is a proposed solution to improve the robustness of Generalized Eigenvalue Proximal Support Vector Machines (GEPSVMs). GEPSVMs are simple and rapid models that have equal significance for all samples, but this can lead to decreased performance when dealing with real-world datasets containing noise and outliers. The IF-GEPSVM assigns intuitionistic fuzzy scores to each training sample based on its location in the high-dimensional feature space, using a kernel function. This leads to simpler optimization problems and reduced computation cost. The proposed model is evaluated on UCI and KEEL datasets, both with and without label noise, and shows superior generalization performance compared to baseline models. The results are supported by statistical analyses and demonstrate the effectiveness of the IF-GEPSVM in practical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new type of machine learning model called the intuitionistic fuzzy generalized eigenvalue proximal support vector machine (IF-GEPSVM) has been developed. This model is an improvement over a previous simpler model called GEPSVM, which can be affected by noisy or outlier data. The IF-GEPSVM gives each piece of training data a score based on where it sits in the big space of features and how close other data points are to it. This makes the optimization problem easier to solve and uses less computer power. The model was tested on some real-life datasets and showed that it could do better than other models at recognizing patterns. |
Keywords
» Artificial intelligence » Generalization » Machine learning » Optimization » Support vector machine
