Explainable artificial intelligence through graph theory by generalized social network analysis-based classifier.

ABSTRACT: We propose a new type of supervised visual machine learning classifier, GSNAc, based on graph theory and social network analysis techniques. In a previous study, we employed social network analysis techniques and introduced a novel classification model (called Social Network Analysis-based Classifier-SNAc) which efficiently works with time-series numerical datasets. In this study, we have extended SNAc to work with any type of tabular data by showing its classification efficiency on a broader collection of datasets that may contain numerical and categorical features. This version of GSNAc simply works by transforming traditional tabular data into a network where samples of the tabular dataset are represented as nodes and similarities between the samples are reflected as edges connecting th

SUBMITTER: Ucer S 

PROVIDER: S-EPMC9458666 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature