NiaClass: Building Rule-Based Classification Models Using Nature-Inspired Algorithms

2021 ◽  
pp. 381-390
Author(s):  
Luka Pečnik ◽  
Iztok Fister ◽  
Iztok Fister
Keyword(s):  
Classification Models ◽  
Rule Based ◽  
Nature Inspired Algorithms

Rule-Based Classification Models of Molecular Autofluorescence

2015 ◽  
Vol 55 (2) ◽  
pp. 434-445 ◽  
Author(s):  
Bo-Han Su ◽  
Yi-Shu Tu ◽  
Olivia A. Lin ◽  
Yeu-Chern Harn ◽  
Meng-Yu Shen ◽  
...  
Keyword(s):  
Classification Models ◽  
Rule Based

Determining the significance of threshold values uncertainty in rule-based classification models

2003 ◽  
Vol 160 (1-2) ◽  
pp. 63-76 ◽  
Author(s):  
Marcela Brugnach ◽  
John Bolte ◽  
G.A Bradshaw
Keyword(s):  
Classification Models ◽  
Threshold Values ◽  
Rule Based

scholarly journals Predicting Student Academic Performance by Means of Associative Classification

2021 ◽  
Vol 11 (4) ◽  
pp. 1420
Author(s):  
Luca Cagliero ◽  
Lorenzo Canale ◽  
Laura Farinetti ◽  
Elena Baralis ◽  
Enrico Venuto
Keyword(s):  
At Risk ◽  
Success Rate ◽  
Learning Analytics ◽  
Empirical Evaluation ◽  
Classification Models ◽  
Success Rates ◽  
Rule Based ◽  
Related Data ◽  
Student Profiles ◽  
Associative Models

The Learning Analytics community has recently paid particular attention to early predict learners’ performance. An established approach entails training classification models from past learner-related data in order to predict the exam success rate of a student well before the end of the course. Early predictions allow teachers to put in place targeted actions, e.g., supporting at-risk students to avoid exam failures or course dropouts. Although several machine learning and data mining solutions have been proposed to learn accurate predictors from past data, the interpretability and explainability of the best performing models is often limited. Therefore, in most cases, the reasons behind classifiers’ decisions remain unclear. This paper proposes an Explainable Learning Analytics solution to analyze learner-generated data acquired by our technical university, which relies on a blended learning model. It adopts classification techniques to early predict the success rate of about 5000 students who were enrolled in the first year courses of our university. It proposes to apply associative classifiers at different time points and to explore the characteristics of the models that led to assign pass or fail success rates. Thanks to their inherent interpretability, associative models can be manually explored by domain experts with the twofold aim at validating classifier outcomes through local rule-based explanations and identifying at-risk/successful student profiles by interpreting the global rule-based model. The results of an in-depth empirical evaluation demonstrate that associative models (i) perform as good as the best performing classification models, and (ii) give relevant insights into the per-student success rate assignments.

scholarly journals Enhanced Random Forest Algorithms for Partially Monotone Ordinal Classification

2019 ◽  
Vol 33 ◽  
pp. 3224-3231 ◽  
Author(s):  
Christopher Bartley ◽  
Wei Liu ◽  
Mark Reynolds
Keyword(s):  
Decision Making ◽  
Random Forest ◽  
User Satisfaction ◽  
Low Complexity ◽  
The State ◽  
Classification Models ◽  
Rule Based ◽  
Ordinal Classification ◽  
Improve Accuracy ◽  
Do So

One of the factors hindering the use of classification models in decision making is that their predictions may contradict expectations. In domains such as finance and medicine, the ability to include knowledge of monotone (nondecreasing) relationships is sought after to increase accuracy and user satisfaction. As one of the most successful classifiers, attempts have been made to do so for Random Forest. Ideally a solution would (a) maximise accuracy; (b) have low complexity and scale well; (c) guarantee global monotonicity; and (d) cater for multi-class. This paper first reviews the state-of-theart from both the literature and statistical libraries, and identifies opportunities for improvement. A new rule-based method is then proposed, with a maximal accuracy variant and a faster approximate variant. Simulated and real datasets are then used to perform the most comprehensive ordinal classification benchmarking in the monotone forest literature. The proposed approaches are shown to reduce the bias induced by monotonisation and thereby improve accuracy.

Resource oriented selection of rule-based classification models: An empirical case study

2006 ◽  
Vol 14 (4) ◽  
pp. 309-338 ◽  
Author(s):  
Taghi M. Khoshgoftaar ◽  
Angela Herzberg ◽  
Naeem Seliya
Keyword(s):  
Classification Models ◽  
Rule Based ◽  
Selection Of

scholarly journals Improvement in Detecting the Fate of Covid-19 Patients and Rule-based Analysis to Discover the Most Important Rules Governing their Fate

2021 ◽  
Author(s):  
Sadegh Ilbeigipour ◽  
Amir Albadvi ◽  
Elham Akhondzadeh Noughabi
Keyword(s):  
Sampling Method ◽  
Class Imbalance ◽  
Supervised Machine Learning ◽  
Classification Models ◽  
Class Imbalance Problem ◽  
Data Set ◽  
Rule Based ◽  
Imbalance Problem ◽  
The World ◽  
Range Of Values

Abstract The world today faces a new challenge that is unprecedented in the last 100 years. The emergence of a new coronavirus has led to a human catastrophe. The new coronavirus is the cause of the Covid-19 disease, which kills many people in the world every day. Scientists in various sciences have been looking for solutions to this problem so far. In addition to general vaccination, maintaining social distance and hygienic principles are the most well-known strategies to prevent Covid-19 infection. In this research, we have tried to examine the symptoms of Covid-19 cases through different supervised machine learning methods. We solved the class imbalance problem using the SMOTE up-sampling method and then developed some classification models to predict the recovery or death of patients. Besides, we implemented a rule-based technique to identify important symptoms that affect patients' fate and calculate the range of values in these features that lead to recovery or death of patients. Our results showed that the random forest model with 94% accuracy, 95.2% sensitivity, 92.7% specification, 93.2% precision, and 94.2% F-score outperforms state-of-the-art classification models. Finally, we identified the ten most significant rules in the data set. The rules state that different combinations of 6 features in certain ranges of their values lead to patients' recovery with 90% confidence. In conclusion, the classification results in this study show better performance than recent researches. Besides, help physicians consider other important factors in improving health services to different groups of Covid-19 patients.

Using Conventional Articulation Tests With Highly Unintelligible Children

1992 ◽  
Vol 23 (1) ◽  
pp. 52-60 ◽  
Author(s):  
Pamela G. Garn-Nunn ◽  
Vicki Martin
Keyword(s):  
Test Results ◽  
Phonological Processes ◽  
Rule Based ◽  
Severity Level ◽  
Error Sensitivity ◽  
Conventional Tests ◽  
Severity Levels ◽  
Conventional Test ◽  
Impaired Children

This study explored whether or not standard administration and scoring of conventional articulation tests accurately identified children as phonologically disordered and whether or not information from these tests established severity level and programming needs. Results of standard scoring procedures from the Assessment of Phonological Processes-Revised, the Goldman-Fristoe Test of Articulation, the Photo Articulation Test, and the Weiss Comprehensive Articulation Test were compared for 20 phonologically impaired children. All tests identified the children as phonologically delayed/disordered, but the conventional tests failed to clearly and consistently differentiate varying severity levels. Conventional test results also showed limitations in error sensitivity, ease of computation for scoring procedures, and implications for remediation programming. The use of some type of rule-based analysis for phonologically impaired children is highly recommended.

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