Ordered weighted aggregation of fuzzy similarity relations and its application to detecting water treatment plant malfunction

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

Shang

et al. 2018

Self Cite

Network intrusion detection systems (IDSs) dynamically monitor communication events on a network, and decide whether any event is symptomatic of an attack or constitutes a legitimate use of the system. They have become an indispensable component of security infrastructure, e.g., to detect threats before widespread damage takes place. A variety of approaches have been proposed to design IDSs, including fuzzy rule-based techniques that offer advantages such as tolerance of noisy and imprecise data. In particular, fuzzy rules can be highly interpretable and trackable if the underlying fuzzy sets are predefined, directly reflecting domain expertise. This paper proposes such an approach to generate a set of weighted fuzzy rules for building effective IDSs, where the rule weights are optimised by Particle Swarm Optimisation without affecting the underlying predefined fuzzy sets. Experiments are performed on benchmark IDS datasets with comparison to alternative systems built with popular machine learning methods.

Section: Optimisation Of Weighted Fuzzy Rules With Particle Swarm mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Weighted Fuzzy Rules Optimised by Particle Swarm for Network Intrusion Detection

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

Shang

et al. 2018

Self Cite

“…Many methods [15,16,17] have been successfully developed in the framework of fuzzy set theory, among which, fuzzy cmeans allows an object belonging to differen clusters to various degrees, overcoming boolean boundaries that are often not natural or even counterintuitive. Each cluster in a fuzzy partition π is a fuzzy set C k , k = 1, · · · , K where C k (x t ) ∈ [0, 1] represents the degree of a data point x t ∈ X belonging to the corresponding fuzzy cluster.…”

Section: Preliminaries Of Fuzzy Clustering Ensemblementioning

confidence: 99%

Dominant-Set-Based Consensus For Fuzzy C-Means Clustering Ensemble

2018 International Conference on Machine Learning and Cybernetics (ICMLC)

et al. 2018

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Conventional clustering approaches partition a set of objects into a certain (some can automatically detect the number of clusters such as DBScan) number of clusters. During the partitioning process, the clusters of objects are produced where each object is assigned to one cluster. On the other hand, the dominantset-based clustering provides a formalisation of clusters by sequentially searching for individual clusters in the set of objects. The resultant clusters do not necessarily form a partition of the set. With the popularity of clustering ensemble, graph-based consensus approaches have been proposed with promising results achieved, many of which are based on the partition of the graph. In this paper, a dominant-set-based consensus method for fuzzy-c-means-based clustering ensemble is proposed. Different from traditional graph-based consensus techniques, the graph generated by the fuzzy clusters are grouped on the basis of the extracted dominant sets. The proposed approach employs a similarity relation to denote the links between component clusters from which the final clusters of ensemble are derived with the extracted dominant set. The proposed method is tested on benchmark data sets against several alternative ensemble methods for fuzzy c-means. The results of experiment show that the proposed dominant-set-based clustering ensemble method generally achieves higher accuracy than its competitors.

“…is the degree to which the objects x i and x j are similar for the feature a ∈ A. Many similarity relations [21] can be constructed for this purpose, for example:…”

Section: Introductionmentioning

confidence: 99%

Sentiment Classification of Drug Reviews Using Fuzzy-rough Feature Selection

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

Shang

et al. 2019

Self Cite

Sentiment analysis mines people's opinions and attitudes regarding a certain issue from source materials. Recently, it has drawn significant attention in a number of application areas. The sentiment analysis of healthcare in general and that of users' drug experience in particular could shed significant light on how to improve public health and make the right decisions. However, one of the major challenges in sentiment classification lies in the very large number of extracted features. Fuzzy-rough feature selection provides a means by which discrete or real-valued noisy data can be effectively reduced without human intervention. This paper proposes an implementation for automatic sentiment classification of drug reviews employing fuzzy rough feature selection. Experimental results demonstrate that the employment of fuzzy-rough feature selection can indeed significantly reduce the complexity of feature space and the classification run-time overheads while maintaining classification accuracy.