To tackle this problem, we propose an algorithm consisting of three phases, namely, i) it first constructs a graph in which each node corresponds to each feature, and each edge has a weight corresponding to mutual information (MI) between features connected by that edge, ii) then perform dominant set clustering to ...
a graph-based approach to feature selection. In this feature selection scheme, the original features are clustered into different dominant-sets based on ...
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Mar 22, 2022 · We evaluate the application of a new feature selection approach to the prediction of molecular activity, based on the construction of an undirected graph.
Using Parzen windows for probability distribution estimation, we then apply the greedy strategy to select the feature that maximizes the.
Feb 6, 2021 · A novel graph-based feature grouping framework with different types of feature relationships. An undirected graph representing features as vertices with edges.
May 31, 2024 · This paper introduces a novel graph-based filter method for automatic feature selection (abbreviated as GB-AFS) for multi-class classification tasks.
A Graph-Based Approach to Feature Selection - ResearchGate
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Abstract · First constructs a graph in which each node corresponds to each feature, · Then perform dominant-set clustering to select a highly coherent set of.
Mar 29, 2023 · Our approach consists of four steps: (i) creation of a document similarity subgraph;. (ii) detection of document communities; (iii) feature ...
We used a graph-based approach, principal component analysis (PCA) and recursive feature elimination to select features for classification from RNAseq datasets.
An approach of feature selection using graph-theoretic heuristic and hill climbing · Computer Science. Pattern Analysis and Applications · 2017.