Enhanced parallel cat swarm optimization based on the Taguchi method

Tsai, Pei‐Wei; Pan, Jeng‐Shyang; Chen, Shyi‐Ming; Liao, Bin-Yih

doi:10.1016/j.eswa.2011.11.117

Cited by 137 publications

(61 citation statements)

References 12 publications

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“…We will also look into the ensemble classification or data stream mining problems using fuzzy rule-based systems where the data instances are challenging, unpredictable and diverse embedded with newly arrived classes. In addition, it is worth to investigate the use of optimization techniques (Chen and Chien 2011;Chen and Kao 2013;Tsai et al 2008Tsai et al , 2012Chen and Chang 2011;Chen and Chung 2006;Chen and Huang 2003) for tuning the shapes of membership functions towards obtaining better performance of prediction.…”

Section: Discussionmentioning

confidence: 99%

Fuzzy rule-based systems for recognition-intensive classification in granular computing context

Liu

Zhang

2018

Granul. Comput.

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In traditional machine learning, classification is typically undertaken in the way of discriminative learning using probabilistic approaches, i.e. learning a classifier that discriminates one class from other classes. The above learning strategy is mainly due to the assumption that different classes are mutually exclusive and each instance is clear-cut. However, the above assumption does not always hold in the context of real-life data classification, especially when the nature of a classification task is to recognize patterns of specific classes. For example, in the context of emotion detection, multiple emotions may be identified from the same person at the same time, which indicates in general that different emotions may involve specific relationships rather than mutual exclusion. In this paper, we focus on classification problems that involve pattern recognition. In particular, we position the study in the context of granular computing, and propose the use of fuzzy rule-based systems for recognition-intensive classification of real-life data instances. Furthermore, we report an experimental study conducted using 7 UCI data sets on life sciences, to compare the fuzzy approach with four popular probabilistic approaches in pattern recognition tasks. The experimental results show that the fuzzy approach can not only be used as an alternative one to the probabilistic approaches but also is capable to capture more patterns which probabilistic approaches cannot achieve.

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Section: Discussionmentioning

confidence: 99%

Fuzzy rule-based systems for recognition-intensive classification in granular computing context

Liu

Zhang

2018

Granul. Comput.

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“…Pei-wei tsai, Jeng-Shyang Pan, Shyi-Ming Chen and Bin-Yih Liao [26] present an enhanced parallel cat swarm optimization (EPCSO) method for solving numerical optimization problems. The parallel cat swarm optimization (PCSO) method is an optimization algorithm designed to solve numerical optimization problems under the conditions of a small population size and a few iteration numbers.…”

Section: Related Workmentioning

confidence: 99%

“…Seeking memory pool (SMP), seeking range of the selected dimension (SRD), counts of dimension to change (CDC) and self-position consideration (SPC) [26]. The process of seeking mode is describes as follow:…”

Section: Seeking Mode: Resting and Observingmentioning

confidence: 99%

Improvement Cat Swarm Optimization for Efficient Motion Estimation

Hadi¹,

Sabah²

2015

IJHIT

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“…This mode is a time for thinking and deciding about next move. This mode has four main parameters which are mentioned as follow: Seeking memory pool (SMP), seeking range of the selected dimension (SRD), counts of dimension to change (CDC) and self-position consideration (SPC) [26]. The process of seeking mode is describes as follow: Step1: Make j copies of the present position of catk, where j = SMP.…”

Section: Seeking Mode: Resting and Observingmentioning

confidence: 99%