SRI: A Scalable Rule Induction Algorithm

Pham, Duc Truong; Afify, A. A.

doi:10.1243/09544062c18304

Cited by 14 publications

(12 citation statements)

References 33 publications

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“…It includes the following: artificial neural networks, support vector machines, and statistical learning. PART PART [22] Slipper Slipper [11] Scalable rule induction induction SRI [45] Rule induction two in one Ritio [63] Rule extraction system version 6 Rule-6 [44] Approximate models Multilayer perceptron MLP [40] C-SVM C-SVM [17] ν-SVM ν-SVM [17] Sequential minimal optimization SMO [47] Radial basis function network RBFN [8] RBFN decremental RBFND [8] RBFN incremental RBFNI [46] Logistic LOG [32] Naïve-Bayes NB [15] Learning vector quantization LVQ [7] Lazy learning Locally weighted learning LWL [4] Lazy learning of Bayesian rules LBR [64] • The third and last group corresponds to the lazy learning category. This group incorporates methods which do not create any model but use the training data to perform the classification directly.…”

Section: Classification Methodsmentioning

confidence: 99%

On the choice of the best imputation methods for missing values considering three groups of classification methods

2011

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In real-life data, information is frequently lost in data mining, caused by the presence of missing values in attributes. Several schemes have been studied to overcome the drawbacks produced by missing values in data mining tasks; one of the most well known is based on preprocessing, formerly known as imputation. In this work, we focus on a classification task with twenty-three classification methods and fourteen different imputation approaches to missing values treatment that are presented and analyzed. The analysis involves a group-based approach, in which we distinguish between three different categories of classification methods. Each category behaves differently, and the evidence obtained shows that the use of determined missing values imputation methods could improve the accuracy obtained for these methods. In this study, the convenience of using imputation methods for preprocessing data sets with missing values is stated. The analysis suggests that the use of particular imputation methods conditioned to the groups is required.

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Section: Classification Methodsmentioning

confidence: 99%

On the choice of the best imputation methods for missing values considering three groups of classification methods

2011

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“…Recalculating the boundary points for each rule is computationally expensive. Moreover, the change of boundary points makes it difficult to apply the search-space pruning rules of the SRI algorithm, which have proved useful for discarding large portions of the search space [23].…”

Section: Proposed Discretization Methodsmentioning

confidence: 99%

Online Discretization of Continuous-Valued Attributes in Rule Induction

Pham

Afify

2005

Proceedings of the Institution of Mechanical Engineers, Part C:

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Machine learning algorithms designed for engineering applications must be able to handle numerical attributes, particularly attributes with real (or continuous) values. Many algorithms deal with continuous-valued attributes by discretizing them before starting the learning process. This paper describes a new approach for discretization of continuousvalued attributes during the learning process. Incorporating discretization within the learning process has the advantage of taking into account the bias inherent in the learning system as well as the interactions between the different attributes. Experiments have demonstrated that the proposed method, when used in conjunction with the SRI rule induction algorithm developed by the authors, improves the accuracy of the induced model.

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“…This substantially increases the efficiency of the learning process. A detailed description of the process by which SRI induces a rule can be found in reference [3]. The SRI algorithm deals with attributes having continuous-values using an on-line discretization approach, in which discretization is an integral component of the learning algorithm.…”

Section: The Sri Algorithmmentioning

confidence: 99%

Three New MDL-Based Pruning Techniques for Robust Rule Induction

Pham

Afify

2006

Proceedings of the Institution of Mechanical Engineers, Part C:

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Overfitting the training data is a major problem in machine learning, particularly when noise is present. Overfitting increases learning time and reduces both the accuracy and the comprehensibility of the generated rules, making learning from large data sets more difficult. Pruning is a technique widely used for addressing such problems and consequently forms an essential component of practical learning algorithms. An important class of pruning techniques is that based on the minimum description length (MDL) principle. This paper presents three new techniques using the MDL principle for pruning rule sets. An important advantage of these techniques is that all of the training data can be used for both inducing and evaluating rule sets. The performance of the techniques are evaluated using three criteria: classification accuracy, rule set complexity, and execution time. This shows that the new techniques, when incorporated into a rule induction algorithm, are more efficient and lead to accurate rule sets that are significantly smaller in size compared with the case before pruning.

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SRI: A Scalable Rule Induction Algorithm

Cited by 14 publications

References 33 publications

On the choice of the best imputation methods for missing values considering three groups of classification methods

On the choice of the best imputation methods for missing values considering three groups of classification methods

Online Discretization of Continuous-Valued Attributes in Rule Induction

Three New MDL-Based Pruning Techniques for Robust Rule Induction

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