Parameter optimization of a fuzzy inference system using the FisPro open source software

Guillaume, Serge; Charnomordic, Brigitte

doi:10.1109/fuzz-ieee.2012.6250821

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…To verify this research hypothesis, the performance provided by the FIS was compared with distinct forecast algorithms: cascade-correlation network (CCN), multilayer perceptron network (MLP), polynomial neural network (GMDH), probabilistic neural network (PNN), radial basis function network (RBFN), gene expression programming (GEP), decision tree forest (Tree Boost), and support vector machine (SVM). All experiments were performed using the R software [53], an open-source simulation tool.…”

Section: Methodsmentioning

confidence: 99%

Fuzzy Artificial Intelligence—Based Model Proposal to Forecast Student Performance and Retention Risk in Engineering Education: An Alternative for Handling with Small Data

et al. 2022

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Understanding the key factors that play an important role in students’ performance can assist improvements in the teaching-learning process. As an alternative, artificial intelligence (AI) methods have enormous potential, facilitating a new trend in education. Despite the advances, there is an open debate on the most suitable model for machine learning applied to forecast student performance patterns. This paper addresses this gap, where a comparative analysis between AI methods was performed. As a research hypothesis, a fuzzy inference system (FIS) should provide the best accuracy in this forecast task, due to its ability to deal with uncertainties. To do so, this paper introduces a model proposal based on AI using a FIS. An online survey was carried to collect data. Filling out a self-report, respondents declare how often they use some learning strategies. In addition, we also used historical records of students’ grades and retention from the last 5 years before the COVID pandemic. Firstly, two experimental groups were composed of students with failing and passing grades, compared by the Mann-Whitney test. Secondly, an association between the ‘frequency of using learning strategies’ and ‘occurrence of failing grades’ was quantified using a logistic regression model. Then, a discriminant analysis was performed to build an Index of Student Performance Expectation (SPE). Considering the learning strategies with greater discriminating power, the fuzzy AI-based model was built using the database of historical records. The learning strategies with the most significant effect on students’ performance were lesson review (34.6%), bibliography reading (25.6%), class attendance (23.5%), and emotion control (16.3%). The fuzzy AI-based model proposal outperformed other AI methods, achieving 94.0% accuracy during training and a generalization capacity of 91.9% over the testing dataset. As a practical implication, the SPE index can be applied as a tool to support students’ planning in relation to the use of learning strategies. In turn, the AI model based on fuzzy can assist professors in identifying students at higher risk of retention, enabling preventive interventions.

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Section: Methodsmentioning

confidence: 99%

Fuzzy Artificial Intelligence—Based Model Proposal to Forecast Student Performance and Retention Risk in Engineering Education: An Alternative for Handling with Small Data

et al. 2022

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“…Parameter optimization allows to optimize all parts of a FIS, using the Solis and Wets algorithm, see [14] for details. As partition parameters and rules have been generated separately, it is interesting to run an optimization procedure of the model as a whole.…”

Section: Optimization and Median Fismentioning

confidence: 99%

Information Processing and Management of Uncertainty in Knowledge-Based Systems

2014

Communications in Computer and Information Science

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Fuzzy logic is a powerful interface between linguistic and numerical spaces. It allows the design of transparent models based upon linguistic rules. The FisPro open source software includes learning algorithms as well as a friendly java interface. In this paper, it is used to model a composite agronomical feature, the vine vigor. The system behavior is characterized by its numerical accuracy and analyzed according to the induced knowledge. Well known input output relationships are identified, but also some rules reflect local interactions.

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“…Then a median FIS was computed, resulting of the combination of the ten optimized FIS; the various optimized parameters were Author-produced version of the article published in Computers and Electronics in Agriculture, 2013, 99, 135-145. The original publication is available at http://www.sciencedirect.com/science/article/pii/S0168169913002275 DOI : 10.1016/j.compag.2013.09.010 replaced by their median value, which is statistically more robust than the mean (Guillaume and Charnomordic, 2012b).…”

Section: Fuzzy Model Optimization and System Performance Evaluationmentioning

confidence: 99%

Imperfect knowledge and data-based approach to model a complex agronomic feature – Application to vine vigor

Coulon‐Leroy¹,

Charnomordic

Thiollet‐Scholtus³

et al. 2013

Computers and Electronics in Agriculture

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Vine vigor, a key agronomic parameter, depends on environmental factors but also on agricultural practices. The goal of this paper is to model vine vigor according to both kinds of influential variables. The perspective is to design a decision support tool to adapt the agricultural practices to the environment in order to get a given vigor target. The approach was based upon a collected dataset and the available expert knowledge. It included a data selection step, which was needed because of data imperfection and incompleteness. Usually implicit in the literature, data selection was carried out with explicit criteria. Then a fuzzy model was designed from the selected data. Owing to the fuzzy model interpretability, its structure and behavior were analyzed to identify input-output relationships and interactions between variables. The case study was located in a French vineyard in the middle Loire valley. The input features were related to soil, rootstock and inter-crop management, the output was an expert assessment of vine plot vigor. Results showed that, despite the data imperfection, the approach was able to select data that yielded an informative model.

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Parameter optimization of a fuzzy inference system using the FisPro open source software

Cited by 6 publications

References 20 publications

Fuzzy Artificial Intelligence—Based Model Proposal to Forecast Student Performance and Retention Risk in Engineering Education: An Alternative for Handling with Small Data

Fuzzy Artificial Intelligence—Based Model Proposal to Forecast Student Performance and Retention Risk in Engineering Education: An Alternative for Handling with Small Data

Information Processing and Management of Uncertainty in Knowledge-Based Systems

Imperfect knowledge and data-based approach to model a complex agronomic feature – Application to vine vigor

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