# Classification and Feature Transformation with Fuzzy Cognitive Maps

arXiv: https://arxiv.org/abs/2103.05124 · HTML: https://mlsanity.com/paper/2103.05124/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 49 references that are in the map.
- First posted on arXiv 8 Mar 2021.

## Builds on (most cited first, 1 of 1)

- [A Gentle Tutorial of Recurrent Neural Network with Error Backpropagation](https://mlsanity.com/paper/1610.02583/) (2016)

## Built on by (most cited first, 3 of 3)

- [Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification](https://mlsanity.com/paper/2107.03423/) (2021)
- [Blind Federated Learning without initial model](https://mlsanity.com/paper/2404.16180/) (2024)
- [Time Series Forecasting Using Fuzzy Cognitive Maps: A Survey](https://mlsanity.com/paper/2201.02297/) (2022)

## Abstract (arXiv)

Fuzzy Cognitive Maps (FCMs) are considered a soft computing technique combining elements of fuzzy logic and recurrent neural networks. They found multiple application in such domains as modeling of system behavior, prediction of time series, decision making and process control. Less attention, however, has been turned towards using them in pattern classification. In this work we propose an FCM based classifier with a fully connected map structure. In contrast to methods that expect reaching a steady system state during reasoning, we chose to execute a few FCM iterations (steps) before collecting output labels. Weights were learned with a gradient algorithm and logloss or cross-entropy were used as the cost function. Our primary goal was to verify, whether such design would result in a descent general purpose classifier, with performance comparable to off the shelf classical methods. As the preliminary results were promising, we investigated the hypothesis that the performance of d-step classifier can be attributed to a fact that in previous d-1 steps it transforms the feature space by grouping observations belonging to a given class, so that they became more compact and separable. To verify this hypothesis we calculated three clustering scores for the transformed feature space. We also evaluated performance of pipelines built from FCM-based data transformer followed by a classification algorithm. The standard statistical analyzes confirmed both the performance of FCM based classifier and its capability to improve data. The supporting prototype software was implemented in Python using TensorFlow library.