An Associative Classifier for Uncertain Datasets

Abstract

Abstract. The classification of uncertain datasets is an emerging research problem that has recently attracted significant attention. Some attempts to devise a classification model with uncertain training data have been proposed using decision trees, neural networks, or other approaches. Among those, the associative classifiers have inspired some of the uncertain classification algorithms given their promising results on standard datasets. We propose a novel associative classifier for uncertain data. Our method, Uncertain Associative Classifier (UAC) is efficient and has an effective rule pruning strategy. Our experimental results on real datasets show that in most cases, UAC reaches better accuracies than the state of the art algorithms.

A Method for Query Top-K Rules from Class Association Rule Set

“…Chen et al proposed a principal association mining (PAM) method to improve the accuracy and the size of classifier [4]. Some efficient methods were also proposed to improve the accuracy such as: using CBA to handle class imbalance [3] and uncertain datasets [10], methods that uses interestingness measures [11,27], a method that uses rule prioritization [5], and a method that uses closed sets [15].…”

Approaches of Handling Uncertain Time Series Data towards Prediction

“…The uncertain time series is also a non-negative and precisely different ways in some fields. Particularly, uncertain data refers to data in which the ambiguity on whether it takes place or not, the existence of the data for the particular attribute values are not ascertained with 100 percent probability [10].…”