SemiSupervised Learning for Class Association Rule Mining Using Genetic Network Programming

Mabu, Shingo; Higuchi, Takuro; Kuremoto, Takashi

doi:10.1002/tee.23109

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…Association rule is an important task to discover frequent patterns in data mining. It has been successfully used in computer network, recommendation system, and medical care [9][10]. Wang et al (2020) studied an improved Apriori algorithm for time series of frequent itemsets, applied it to mining association rules based on time constraints, and concluded that this algorithm was superior to the traditional algorithm in storage space [11].…”

Section: Introductionmentioning

confidence: 99%

Sustainable Development of College and University Education by use of Data Mining Methods

Wang

Chung

2021

Int. J. Emerg. Technol. Learn.

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To improve the education efficiency of the students, the student-centered education plan is explored. First, the Apriori algorithm of association rules is used to mine the potential related patterns in the score data of college students and establish a reasonable teaching method. Second, aided by the decision tree model, the factors affecting students' academic performance are studied, and the potential relationship between different courses is studied. Finally, the Apriori algorithm of association rules combined with decision tree model is used to generate the early warning mechanism of students' achievement, and the course performance of college students is empirically analyzed. The results show that: C language has two sides of dependence on many subjects; higher mathematics → linear algebra → mathematical statistics → computer composition principle → computer network. The teaching scheme of C language → C + + → Java more conforms to the learning mechanism of college students. Through empirical analysis, the early warning mechanism of association rule Apriori algorithm and decision tree model can effectively analyze student's course and give student's achievement. It is found that the method proposed can provide theoretical basis for students, teachers, and university administrators to carry out education reform and education management decision-making, improve students' performance and education quality, and realize the "student-oriented" education concept, so it can be applied to the actual education management.

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Section: Introductionmentioning

confidence: 99%

Sustainable Development of College and University Education by use of Data Mining Methods

Wang

Chung

2021

Int. J. Emerg. Technol. Learn.

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“…However, feature selection can reduce the requirement repository and enhance the performance of the algorithm by removing the redundancies and noise [34]. An associative classification, which is a combination of unsupervised learning methods, such as the FP-Growth algorithm or association rule and NB classifiers, performs much better than the standalone classification method [35]. The hybrid of the FP-Growth algorithm and K-nearest neighbor (KNN) can obtain a high classification accuracy [36].…”

Section: Proposed Identification Modelmentioning

confidence: 99%

Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques

Ghani

Noma²,

Mohammed

et al. 2021

Sustainability

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Physicians depend on their insight and experience and on a fundamentally indicative or symptomatic approach to decide on the possible ailment of a patient. However, numerous phases of problem identification and longer strategies can prompt a longer time for consulting and can subsequently cause other patients that require attention to wait for longer. This can bring about pressure and tension concerning those patients. In this study, we focus on developing a decision-support system for diagnosing the symptoms as a result of hearing loss. The model is implemented by utilizing machine learning techniques. The Frequent Pattern Growth (FP-Growth) algorithm is used as a feature transformation method and the multivariate Bernoulli naïve Bayes classification model as the classifier. To find the correlation that exists between the hearing thresholds and symptoms of hearing loss, the FP-Growth and association rule algorithms were first used to experiment with small sample and large sample datasets. The result of these two experiments showed the existence of this relationship, and that the performance of the hybrid of the FP-Growth and naïve Bayes algorithms in identifying hearing-loss symptoms was found to be efficient, with a very small error rate. The average accuracy rate and average error rate for the multivariate Bernoulli model with FP-Growth feature transformation, using five training sets, are 98.25% and 1.73%, respectively.

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“…Experimentally obtained results using two benchmark datasets revealed that their method provided competitively high detection rates when compared with other ML techniques and GNP with the approach of a current cross-industry standard processing for data mining [28]. As the latest study, Mabe et al [29] combined GNP with a semisupervised learning framework to extract class association rules from a small number of labeled data and from numerous unlabeled data. The experimentally obtained results obtained using several benchmark datasets revealed the classification accuracy of their method as superior to that of conventional methods.…”

Section: Related Studiesmentioning

confidence: 99%

Automatic Calibration of Piezoelectric Bed-Leaving Sensor Signals Using Genetic Network Programming Algorithms

2021

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This paper presents a filter generating method that modifies sensor signals using genetic network programming (GNP) for automatic calibration to absorb individual differences. For our earlier study, we developed a prototype that incorporates bed-leaving detection sensors using piezoelectric films and a machine-learning-based behavior recognition method using counter-propagation networks (CPNs). Our method learns topology and relations between input features and teaching signals. Nevertheless, CPNs have been insufficient to address individual differences in parameters such as weight and height used for bed-learning behavior recognition. For this study, we actualize automatic calibration of sensor signals for invariance relative to these body parameters. This paper presents two experimentally obtained results from our earlier study. They were obtained using low-accuracy sensor signals. For the preliminary experiment, we optimized the original sensor signals to approximate high-accuracy ideal sensor signals using generated filters. We used fitness to assess differences between the original signal patterns and ideal signal patterns. For application experiments, we used fitness calculated from the recognition accuracy obtained using CPNs. The experimentally obtained results reveal that our method improved the mean accuracies for three datasets.

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SemiSupervised Learning for Class Association Rule Mining Using Genetic Network Programming

Cited by 6 publications

References 14 publications

Sustainable Development of College and University Education by use of Data Mining Methods

Sustainable Development of College and University Education by use of Data Mining Methods

Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques

Automatic Calibration of Piezoelectric Bed-Leaving Sensor Signals Using Genetic Network Programming Algorithms

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