This repository contains source code to the article: Piotr Szwed: Classification and feature transformation with Fuzzy Cognitive Maps, Applied Soft Computing, Elsevier 2021
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.
The code was written in 2018 using TensorFlow 1.6 library, however recently it was ported to TF 2.6.
Current configuration:
- Python 3.8.3
- scikit-learn 0.24.2
- scipy 1.5.0
- numpy 1.19.3
- Tensorflow 2.6.0
basepackage comprises the code of FCM classifier, binary and multiclass classifiers are defined inbinary_classifier.pyandmc_classifier.pyutilpackage provides access to datasets used during experimentsuse_cases.multiclasscv_fcm_transformer.py- run this script. It implements the pipeline in Fig. 7 pg. 20gmm_classifier.py- implementation of a classifier based on a mixture of Gaussian distributions (used during experiments)fcm_best_params.py- a Bunch (dictionary) of parameters for particular datasets. These parameters were established manually, typically by random search
After launching cv_fcm_transformer.py a file results_fcm_transformer.py is created in the folder use_cases/multiclass/results. It comprises a giant multilevel dictionary gathering detailed results data.
The current version was tested on Windows 10 laptop equiped with 4GB GPU. The whole data processing took about 5 hours. However, the TF1.6 version was tested on Windows and VMware virtual Linux machine not using GPU. The VMware environment was more efficient.
During an execution two waring messages frequently appear.
W tensorflow/core/data/root_dataset.cc:167] Optimization loop failed: Cancelled: Operation was cancelled
validation.py:70: FutureWarning: Pass labels=[0 1 2 3 4 5 6 7 8 9] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error warnings.warn(f"Pass {args_msg} as keyword args. From version
@article{szwed2021classification,
title={Classification and feature transformation with Fuzzy Cognitive Maps},
author={Szwed, Piotr},
journal={Applied Soft Computing},
volume={105},
pages={107271},
year={2021},
publisher={Elsevier}
}