A decision-tree-based symbolic rule induction system for text categorization

Johnson, David; Oles, Frank J.; Zhang, T.; Goetz, Thomas

doi:10.1147/sj.413.0428

Cited by 88 publications

(31 citation statements)

References 13 publications

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“…The transformation of decision trees to rule-based classifiers is discussed generally in [106], and for the particular case of text data in [68]. For each path in the decision tree a rule can be generated, which repre-sents the conjunction of the predicates along that path.…”

Section: Rule-based Classifiersmentioning

confidence: 99%

A Survey of Text Classification Algorithms

2012

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Section: Rule-based Classifiersmentioning

confidence: 99%

A Survey of Text Classification Algorithms

2012

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“…1b) the point X best-fits into class 1 (blue circles) according to a majority vote of the five nearest points. A decision tree (Apte et al 1998;Johnson et al 2002) is a method of visually and explicitly representing decisions and decision making. The goal is to create a model that predicts the value of a target variable, based on several input variables.…”

Section: Text Categorization Based On Machine Learning Methodsmentioning

confidence: 99%

A comparative study of evolving fuzzy grammar and machine learning techniques for text categorization

et al. 2014

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Several methods have been studied in text categorization and mostly are inspired by the statistical distribution features in the texts, such as the implementation of Machine Learning (ML) methods. However, there is no work available that investigates the performance of ML-based methods against the text expression-based method, especially for incident and medical case categorization. Meanwhile, these two domains are becoming ever more popular, due to a growing interest of automation in security intelligence and health services. This paper presents a text expression-based method called Evolving Fuzzy Grammar (EFG) and evaluates its performance against the conventional ML methods of Naïve Bayes, support vector machine, k-nearest neighbor, adaptive booting, and decision tree. The incident dataset used is a real dataset that was taken from the World Incidents Tracking System, while ImageCLEF 2009 was used as the source for radiology case reports. The results suggested variations of strength and weakness of each method in both cateCommunicated by V. Loia. gorization tasks, where a standard evaluation technique (i.e., recall, precision, and F-measure) was used. In both domains, the SMO and IBk methods were the best, while AdaBoost was the worst. It was also observed that the medical dataset was easier to categorize than the incident. Although EFG was ranked second lowest, it obtained the highest precision score in the bombing categorization, the highest score in armed attack recall, and was averagely ranked in the top three for the medical case categorization. It was also noted that the text expression-based method used in EFG was the most verbose and expressive, when compared to the ML methods. This indicates that EFG is a viable method in text categorization and may serve as an alternative approach to such a task.

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“…A decision-tree learning algorithm based on Gini impurity is used for classification on the basis of its readily-interpretable results (rule induction using decision tree algorithms [17], has the characteristic of direct translatability into logical clauses). Gini impurity measures the degree of impurity in a given dataset comprising multiple class labels, i.e., it measures the probability of a randomly chosen element to be incorrectly labelled given a subset of randomly distributed class labels.…”

Section: A Decision Tree Learningmentioning

confidence: 99%