Optimizing the modified fuzzy ant-miner for efficient medical diagnosis

Aribarg, Thannob; Supratid, Siriporn; Lursinsap, Chidchanok

doi:10.1007/s10489-011-0332-x

Cited by 23 publications

(5 citation statements)

References 36 publications

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“…The main idea of the models presented in the following sections is that we require that the "possibility" of achieving a goal is sufficiently high, or equivalently we require that the "necessity" that an event happens (the health condition or wellness of a person improves) is higher than a predefined threshold. It is important to mention here that, beside a few cases [28], there is a lack of research on applying fuzzy optimization tools in wellness or healthrelated decision problems. For overcoming this lack of research, we propose to use possibilistic chance constrained programming models [22] in developing health-related decision support systems.…”

Section: Representing Wellness Related Data With (Interval-valued) Fu...mentioning

confidence: 99%

Fuzzy optimization to improve mobile health and wellness recommendation systems

Mezei

Nikou

2018

Knowledge-Based Systems

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Section: Representing Wellness Related Data With (Interval-valued) Fu...mentioning

confidence: 99%

Fuzzy optimization to improve mobile health and wellness recommendation systems

Mezei

Nikou

2018

Knowledge-Based Systems

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“…In addition to the previously mentioned AntMiner series including its variations (Martens et al 2007(Martens et al , 2011Tripathy et al 2013;Aribarg et al 2012), the literature also includes other ACO rule-based classifiers. Perhaps the most notable example is Ant-labeler, a semisupervised method for assigning labels to unlabelled data (Albinati et al 2015).…”

Section: Rule Discovery Classificationmentioning

confidence: 99%

PolyACO+: a multi-level polygon-based ant colony optimisation classifier

et al. 2017

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Ant colony optimisation (ACO) for classification has mostly been limited to rulebased approaches where artificial ants walk on datasets in order to extract rules from the trends in the data, and hybrid approaches which attempt to boost the performance of existing classifiers through guided feature reductions or parameter optimisations. A recent notable example that is distinct from the mainstream approaches is PolyACO, which is a proof-ofconcept polygon-based classifier that resorts to ACO as a technique to create multi-edged polygons as class separators. Despite possessing some promise, PolyACO has some significant limitations, most notably, the fact of supporting classification of only two classes, including two features per class. This paper introduces PolyACO+, which is an extension of PolyACO in three significant ways: (1) PolyACO+ supports classifying multiple classes, (2) PolyACO+ supports polygons in multiple dimensions enabling classification with more than two features, and (3) PolyACO+ substantially reduces the training time compared to Poly-ACO by using the concept of multi-levelling. This paper empirically demonstrates that these updates improve the algorithm to such a degree that it becomes comparable to state-of-the-art techniques such as SVM, neural networks, and AntMiner+.

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“…Fuzzy ant-miner that provides a fuzzy mining framework for the automatic extraction of fuzzy rules from labeled numerical data is discussed in [4]. [16].…”

Section: A Medical Diagnosismentioning

confidence: 99%