Classification and Feature Transformation with Fuzzy Cognitive Maps

03/08/2021
by   Piotr Szwed, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/08/2021

Symptom based Hierarchical Classification of Diabetes and Thyroid disorders using Fuzzy Cognitive Maps

Fuzzy Cognitive Maps (FCMs) are soft computing technique that follows an...
research
01/07/2022

Time Series Forecasting Using Fuzzy Cognitive Maps: A Survey

Among various soft computing approaches for time series forecasting, Fuz...
research
06/07/2022

PyTSK: A Python Toolbox for TSK Fuzzy Systems

This paper presents PyTSK, a Python toolbox for developing Takagi-Sugeno...
research
12/07/2022

Development Of A Fire Detection System On Satellite Images

This paper discusses the development of a convolutional architecture of ...
research
05/16/2023

Measuring Implicit Bias Using SHAP Feature Importance and Fuzzy Cognitive Maps

In this paper, we integrate the concepts of feature importance with impl...

Please sign up or login with your details

Forgot password? Click here to reset