A fuzzy association rule-based classifier for imbalanced classification problems

Sanz, Josean; Sesma‐Sara, Mikel; Bustince, Humberto

doi:10.1016/j.ins.2021.07.019

Cited by 26 publications

(7 citation statements)

References 41 publications

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“…Likewise, the output in the system itself can be the input for the supply chain information system. Combined framework of supply chain and expert system using rule-based reasoning method can encourage better system work [17], [41]. The combined architecture in this research can be seen in Figure 5.…”

Section: The Combining Of Supply Chain and Expert Systemmentioning

confidence: 97%

The new model for medicine distribution by combining of supply chain and expert system using rule-based reasoning method

Mufadhol

Mustafid²,

Jie³

et al. 2023

IJ-AI

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The medicine distribution supply chain is important, especially during the COVID-19 pandemic, because delays in medicine distribution can increase the risk for patients. So far, the distribution of medicines has been carried out exclusively and even some medicines are distributed on a limited basis because they require strict supervision from the Medicine Supervisory Agency in each department. However, the distribution of this medicine has a weakness if at one public Health center there is a shortage of certain types of medicines, it cannot ask directly to other public Health center, thus allowing the availability of medicines not to be fulfilled. An integrated process is needed that can accommodate regulations and leadership policies and can be used for logistics management that will be used in medicine distribution. This study will create a new model by combining supply chains with information systems and expert systems using the rule-based reasoning method as an inference engine that can be developed for medicine distribution based on a mobile hybrid system in the Demak District Health Office, Indonesia. So that a new framework model based on a mobile hybrid system can facilitate the distribution of medicines effectively and efficiently.

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Section: The Combining Of Supply Chain and Expert Systemmentioning

confidence: 97%

The new model for medicine distribution by combining of supply chain and expert system using rule-based reasoning method

Mufadhol

Mustafid²,

Jie³

et al. 2023

IJ-AI

View full text Add to dashboard Cite

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“…The goal of association rule mining is to generate certain associative relationships between data items with degrees of confidence and support greater than user-specified thresholds. Classification problems have been widely tackled using fuzzy techniques [1]- [2] and data stream classification has become a crucial online learning issue in recent years. Most of these data are characterized as high speed, infinite length, and nonstationary data distribution.…”

Section: Motivationmentioning

confidence: 99%

“…In this section, we first review the known methods for processing imbalance data stream with concept drift (section 2.1). In the Section 2.2, we describe FRBCSs focusing on the FARC-HD [1] and FARCI [2] algorithms, since they are the basis of our new method.…”

Section: Related Workmentioning

confidence: 99%

EFR-IC: Ensemble Fuzzy association Rule-based classifier for Imbalanced data streams with Concept drift

Roshanfekr,

Razzazi

2023

Preprint

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One of the most contestable problems in online learning is concept drift. In addition, if the data stream has imbalanced data, the detection of concept drift is more difficult, especially, when drift is in minority samples. Ensemble classifiers are also effective for the data stream classification with concept drift. By adjusting the weight to every individual classifier, we can manage the concept drift and misclassification problems. Using association rule mining techniques can help in balancing datasets and detecting concept drift in the early levels. In this article, we propose an Ensemble Fuzzy association Rule-based Classifier for Imbalanced data with Concept drift (EFR-IC) to deal with imbalanced streaming data containing concept drift. EFR-IC has five advantages compared with the existing methods as follows: 1) it does not need the data from previous chunks so in terms of storage space is more economical than similar methods; 2) it is stable in stationary and nonstationary environments; 3) due to the synchronization of all steps of algorithm execution -handling imbalanced data, concept drift detection, classification- execution speed is much better than similar methods; 4) it can be adapted to the new condition when swapping majority class to minority class; 5) it can timely react to multiple kinds of concept drifts. Experiments on both real and synthetic datasets containing concept drift show the effectiveness of EFR-IC in learning nonstationary imbalanced data sets.

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“…In the distributed subspace of English-Chinese long sentences under the constraint of the mixed corpus, considering the relevance of resource attributes, the fuzzy set [14] of English-Chinese long sentences under the constraint of the mixed corpus is obtained by the method of fuzzy association rules scheduling, and the semantic correlation function of grammar analysis with the largest semantic correlation of English-Chinese long sentences under the constraint of the mixed corpus is obtained as follows: Mobile Information Systems…”

Section: Concept Tree Of Context Semantic Mapping Of Machinementioning

confidence: 99%

Research on Machine Translation Method of English-Chinese Long Sentences Based on Fuzzy Semantic Optimization

Dong

2022

Mobile Information Systems

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The machine translation of English-Chinese long sentences is out of order under the constraint of the mixed corpus. In order to improve the accuracy of machine translation of English-Chinese long sentences, a machine translation method based on fuzzy semantic optimization is proposed. By adopting the method of systematic semantic configuration structure fusion, the ontology mapping model of the English-Chinese long sentence subject list under the constraint of the mixed corpus is constructed. In the distribution structure space of English-Chinese long sentences under the constraint of the mixed corpus, the method of extracting the best semantic relevance and analyzing the concept tree of context semantic mapping is adopted to mine the dynamic features of the subject list of English-Chinese long sentence translation under the constraint of the mixed corpus. In the intrinsic distribution sequence of semantic mapping between concepts, the fuzzy semantic combination features of English-Chinese long sentences under the constraint of the mixed corpus are extracted. The distribution results of word knowledge priority selection sequence, the automatic translation of English-Chinese long sentences under the constraint of the mixed corpus is realized by scheduling a grammatical error correction task list. According to the simulation results, machine translation of long English-Chinese sentences under the restriction of mixed corpora is capable of good semantic configuration. The degree of linguistic structural similarity between English and Chinese is great, which enhances the automatic translation capacity of English-Chinese long sentences under the limitation of the mixed corpus.

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A fuzzy association rule-based classifier for imbalanced classification problems

Cited by 26 publications

References 41 publications

The new model for medicine distribution by combining of supply chain and expert system using rule-based reasoning method

The new model for medicine distribution by combining of supply chain and expert system using rule-based reasoning method

EFR-IC: Ensemble Fuzzy association Rule-based classifier for Imbalanced data streams with Concept drift

Research on Machine Translation Method of English-Chinese Long Sentences Based on Fuzzy Semantic Optimization

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