Fuzzy hierarchical approach to odor classification

D. Dumitrescu; Beatrice Lazzerini; F. Marcelloni

doi:10.1117/12.343056

22 March 1999 Fuzzy hierarchical approach to odor classification

D. Dumitrescu, Beatrice Lazzerini, F. Marcelloni

Proceedings Volume 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks; (1999) https://doi.org/10.1117/12.343056
Event: Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Neural Networks Fuzzy Systems, Evolutionary Systems and Virtual Re, 1998, Stockholm, Sweden

Abstract

A fuzzy logic-based system to classify olfactive signals is presented. The odor samples are obtained from an electronic noise that contains conducting polymer sensors with partially overlapping sensitivities to odors. The sensor responses are represented by means of the coefficients of their Fast Fourier Transform (FFT). A feature reduction method is applied to reduce the feature space dimension. Then, an unsupervised Fuzzy Divisive Hierarchical Clustering (FDHC) method is used to establish the optimal number of clusters in the data set as well as the optimal cluster structure. The output of FDHC is a binary hierarchy of fuzzy classes that are used to build a supervised fuzzy hierarchical classifier. At each level of the fuzzy hierarchy a separating hyperplane of the two corresponding fuzzy training classes is determined. The hyperplane identifies two crisp decision regions, which will be refined at the next level of the hierarchy. In this way, we obtain a hierarchy of regions, which defines a crisp decision tree. Each region is, therefore, related to a specific expected output of the system. Recognition of an unknown odor is accomplished by computing the FFT of the corresponding signal and using the decision tree to establish the region the odor belongs to. Two small-scale applications of the method yielded 100% classification accuracy on out-of-sample data.

Citation Download Citation

D. Dumitrescu, Beatrice Lazzerini, and F. Marcelloni "Fuzzy hierarchical approach to odor classification", Proc. SPIE 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks, (22 March 1999); https://doi.org/10.1117/12.343056

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available