RULEM: A novel heuristic rule learning approach for ordinal classification with monotonicity constraints

Verbeke, Wouter; Martens, David; Baesens, Bart

doi:10.1016/j.asoc.2017.01.042

Cited by 15 publications

(17 citation statements)

References 38 publications

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“…Previous experiments involving RULEM have been focused on algorithms that employ the sequential covering technique, which generally ignores rule interactions when constructing a model. In fact this is one of the reasons RULEM authors focused on post-processing, as monotonicity is a global property [14]. However, cAnt-Miner PB and its derivatives generate an entire rule list in each iteration of the algorithm.…”

Section: A Monotonic Experimental Resultsmentioning

confidence: 99%

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Post-Processing Methods to Enforce Monotonic Constraints in Ant Colony Classification Algorithms

Brookhouse

Otero

2018

2018 International Joint Conference on Neural Networks (IJCNN)

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Section: A Monotonic Experimental Resultsmentioning

confidence: 99%

“…3) Additive monotonic post-processing with RULEM: Verbeke et al introduced a new algorithm, RULEM [14], that tackles the monotonic problem in a different way. While still a post-processing technique, RULEM adds additional new rules to the list of rules to force monotonic behaviour.…”

Section: Enforcing Monotonicity Constrainsmentioning

confidence: 99%

Post-Processing Methods to Enforce Monotonic Constraints in Ant Colony Classification Algorithms

Brookhouse

Otero

2018

2018 International Joint Conference on Neural Networks (IJCNN)

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“…• Rule Learning of ordinal classification with Monotonicity constraints (RULEM [48]). The authors present a technique to induce monotonic ordinal rule based classification models, which can be applied in combination with any rule or tree induction technique in a post processing step.…”

Section: Decision Trees and Classification Rulesmentioning

confidence: 99%

Monotonic classification: An overview on algorithms, performance measures and data sets

et al. 2019

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Currently, knowledge discovery in databases is an essential step to identify valid, novel and useful patterns for decision making. There are many real-world scenarios, such as bankruptcy prediction, option pricing or medical diagnosis, where the classification models to be learned need to fulfill restrictions of monotonicity (i.e. the target class label should not decrease when input attributes values increase). For instance, it is rational to assume that a higher debt ratio of a company should never result in a lower level of bankruptcy risk. Consequently,

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“…In the last approach, problem-specific techniques [14][15][16][17][18] were developed for ordinal data classification by modifying present classification algorithms. The main advantage of this approach is to retain the order among the class labels.…”

Section: Related Workmentioning

confidence: 99%

“…They applied their proposed method on the field of sentiment analysis and presented effective results for complex datasets. Researchers in another study [17] presented a novel heuristic rule learning approach with monotonicity constraints including two novel justifiability measures for ordinal classification. The experiments were performed to test the proposed approach and the results indicated that the novel method showed high prediction performance by guaranteeing monotone classification with low rule set increase.…”

Section: Related Workmentioning

confidence: 99%

EBOC: Ensemble-Based Ordinal Classification in Transportation

Yıldırım

Birant

2019

Journal of Advanced Transportation

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Learning the latent patterns of historical data in an efficient way to model the behaviour of a system is a major need for making right decisions. For this purpose, machine learning solution has already begun its promising marks in transportation as well as in many areas such as marketing, finance, education, and health. However, many classification algorithms in the literature assume that the target attribute values in the datasets are unordered, so they lose inherent order between the class values. To overcome the problem, this study proposes a novel ensemble-based ordinal classification (EBOC) approach which suggests bagging and boosting (AdaBoost algorithm) methods as a solution for ordinal classification problem in transportation sector. This article also compares the proposed EBOC approach with ordinal class classifier and traditional tree-based classification algorithms (i.e., C4.5 decision tree, RandomTree, and REPTree) in terms of accuracy. The results indicate that the proposed EBOC approach achieves better classification performance than the conventional solutions.

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RULEM: A novel heuristic rule learning approach for ordinal classification with monotonicity constraints

Cited by 15 publications

References 38 publications

Post-Processing Methods to Enforce Monotonic Constraints in Ant Colony Classification Algorithms

Post-Processing Methods to Enforce Monotonic Constraints in Ant Colony Classification Algorithms

Monotonic classification: An overview on algorithms, performance measures and data sets

EBOC: Ensemble-Based Ordinal Classification in Transportation

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