Optimized Fuzzy min-max neural network: an efficient approach for supervised outlier detection

Title:

Optimized Fuzzy min-max neural network: an efficient approach for supervised outlier detection

Creator:

Upasani, Nilam and Om, Hari

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:48f2b365-4e70-4ed8-9b58-5261be1a9145
uuid:48f2b365-4e70-4ed8-9b58-5261be1a9145
doi:10.14311/NNW.2018.28.017

Subject:

coarse-grained knowledge, fault detection, Fuzzy Min-Max Neural Network (FMN), intrusion detection, neuro-fuzzy systems, and supervised outlier detection

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Fuzzy min-max neural network (FMN), proposed by Simpson is a well-known supervised neuro-fuzzy classifier that has been successfully used by many researchers for pattern recognition. However, the FMN represents the learned knowledge with exhaustive details in a `fine-grained' manner that reduces its performance for pattern recognition in terms of the recall time per pattern. In this paper, we adapt the basic architecture of the FMN to represent the learned knowledge in a compact way that is in a `coarse-grained' manner, which is closed to human thinking. The working of the proposed method that is fuzzy min-max neural network with knowledge compaction (FMN-KC) is illustrated using the Fisher Iris dataset. The potential of using the FMN-KC for supervised outlier detection is demonstrated using a time-series disk defect dataset published by NASA and KDD cup 99 dataset available in UCI repository. The proposed method achieves around 50% gain in the recall time as compared to the original FMN and the recognition rate is also comparable. We strongly recommend using the proposed architecture FMN-KC for supervised outlier detection in the real time applications, where recall time per pattern is one of the key parameters.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2018 Volume:28 | Number:4

Harvested from:

CDK

Metadata only:

false