nTOPSIS: Developing a multivariate multiclass unsupervised classifier using an ensemble of TOPSIS modules
Year: 2026
Type: article
Abstract: Accurate image and object classification in precision agriculture research, as well as other fields, has been achieved using supervised and semi-supervised learning methods. These methods require preparing training datasets, training classification models, and tuning their hyperparameters. The methodological contribution of this research is the development of a novel trainingless and hyperparameter-free classification method based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This research transforms TOPSIS from multi-criteria decision-making (alternative ranking) to unsupervised classification. The novel method, denoted nTOPSIS, uses a network of TOPSIS modules to predict object classes; hence the (n) prefix. The nTOPSIS classifier is developed for offline implementations on edge-computers to solve precision agriculture problems. It serves as a general-purpose, easy-to-use, and lightweight classifier for tabular datasets. The performance of the nTOPSIS classifier is compared to four supervised classifiers: KNN, SVM, RF, and XGBoost, in addition to the zero-shot transformer-based TabPFN. The classification results of four UCI datasets: Iris (and Gaussian-augmented Iris), Palmer Penguins, Rice Cultivars, and Mammographic Mass, are reported. The performance of the nTOPSIS unsupervised classifier was on par with the benchmarking classifiers, achieving weighted accuracy scores of [0.96, 0.98, 0.98, 0.92, 0.81] across the respective datasets without optimization. Also, the nTOPSIS classifier was faster than TabPFN and XGBoost. This work constitutes a novel contribution to unsupervised classification methods. • nTOPSIS is a general-purpose multivariate and multiclass unsupervised classifier. • Combines multiple TOPSIS modules into a classification network. • Provides trainingless, data-independent, and hyperparameter-free classification. • Conforms to the Confusion Matrix performance metrics. • Classifies observations based on the similarity of their attributes to actual class attributes. • Generates class-wise confidence scores akin to supervised classifiers.
Authors: Radhwan Sani, Tamer Rabie, Ali Cheaitou
Institution: University of Sharjah
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,790
OpenAlex ID: W7147209319