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Design of fuzzy rule-based classifiers through granulation and consolidation

Riid A., Preden J.-S.

Journal of Artificial Intelligence and Soft Computing Research

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2017

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Vol. 7, No. 2

137--147

EN

This paper addresses the issue how to strike a good balance between accuracy and compactness in classification systems - still an important question in machine learning and data mining. The fuzzy rule-based classification approach proposed in current paper exploits the method of rule granulation for error reduction and the method of rule consolidation for complexity reduction. The cooperative nature of those methods - the rules are split in a way that makes efficient rule consolidation feasible and rule consolidation itself is capable of further error reduction - is demonstrated in a number of experiments with nine benchmark classification problems. Further complexity reduction, if necessary, is provided by rule compression.

2

Combining Multiple Sound Sources Localization Hybrid Algorithm and Fuzzy Rule Based Classification for Real-time Speaker Tracking Application

Ibala C, Astapov S, Bettens F, Escobar F, Chang X, Valderrama C, Riid A

International Journal of Microelectronics and Computer Science

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2013

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Vol. 4, nr 1

12--25

EN

This work present a novel approach to track a specific speaker among multiple using the Minimum Variance Distortionless Response (MVDR) beamforming and fuzzy logic ruled based classification for speaker recognition. The Sound sources localization is performed with an improve delay and sum beamforming (DSB) computation methodology. Our proposed hybrid algorithm computes first the Generalized Cross Correlation (GCC) to create a reduced search spectrum for the DSB algorithm. This methodology reduces by more than 70% the DSB localization computation burden. Moreover for high frequencies Sound sources beamforming, the DSB will be preferred to the MVDR for logic and power consumption reduction.

3

A Multistage Procedure of Mobile Vehicle Acoustic Identification for Single-Sensor Embedded Device

Astapov S., Riid A.

International Journal of Electronics and Telecommunications

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2013

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Vol. 59, No. 2

151-160

EN

Mobile vehicle identification has a wide application field for both civilian and military uses. Vehicle identification may be achieved by incorporating single or multiple sensor solutions and through data fusion. This paper considers a single-sensor multistage hierarchical algorithm of acoustic signal analysis and pattern recognition for the identification of mobile vehicles in an open environment. The algorithm applies several standalone techniques to enable complex decision-making during event identification. Computationally inexpensive procedures are specifically chosen in order to provide real-time operation capability. The algorithm is tested on pre-recorded audio signals of civilian vehicles passing the measurement point and shows promising classification accuracy. Implementation on a specific embedded device is also presented and the capability of real-time operation on this device is demonstrated.