Improvement of Decision Accuracy Using Discretization of Continuous Attributes

A discretization algorithm based on Class-Attribute Contingency Coefficient

“…Among the several functions of data mining, classification is crucially important and has been applied successfully to several areas such as automatic text summarization and categorization [17,38], image classification [15], and virus detection of new malicious emails [31]. Although real-word data mining tasks often involve continuous attributes, some classification algorithms such as AQ [18,26], CLIP [6,7] and CN2 [8] can only handle categorical attribute, while others can handle continuous attributes but would perform better on categorical attributes [36]. To deal with this problem, a lot of discretization algorithms have been proposed [11,12,22,28].…”

“…Their experiments showed that the accuracy of these compact decision trees was also preserved. Wu et al [36] defined a distributional index and then proposed a dynamic discretization algorithm to enhance the decision accuracy of naïve Bayes classifiers. However, the advantage of static approaches as opposed to dynamic approaches is the independence from the learning algorithms [24].…”

A Study of Improving the Performance of Mining Multi-Valued and Multi-Labeled Data

“…Supervised methods discretize attributes with the consideration of class information, while unsupervised methods do not (Dougherty et al, 1995;Ferreira and Figueiredo, 2011;Jiang and Yu, 2009;Zeng et al, 2011). Dynamic methods (Wu et al, 2006) discretize continuous attributes when a classifier is being built while in static methods discretization is completed prior to the learning task. Global methods (Zeng et al, 2011), which use total records to generate the discretization scheme, are usually associated with static methods.…”

Knowledge in Imperfect Data

“…(Shi & Fu 2005, Boullé, 2006Ekbal, 2006;Wu QX et al, 2006;Jin et al, 2009;Mitov et al, 2009), and on production data (Perzyk, 2005).…”