Various attributes within a dataset relate to each other and with the class attribute. The relationship between the different attributes with class attribute may improve the classification accuracy. The paper introduces CCSA algorithm that performs the clustering that is cascaded by classification based on association. The Clustering process generates a group of various instances within the dat...

The Internet has been in a state of explosive expansion over the last decade and a half. The addition of numerous web pages to the World Wide Web by a vast array of authors on a plethora of topics leaves behind the problem of organizing these web pages in order to improve search results leading to more relevant information. In this paper, a modified attribute weighted dynamic k-Nearest Neighbor...

Incremental Attribute Learning (IAL) is a feasible approach for solving high-dimensional pattern recognition problems. It gradually trains features one by one. Previous research indicated that supervised machine learning with input attribute ordering can improve classification results. Moreover, input space partitioning can also effectively reduce the interference among features. This study pro...

OBJECTIVE The goal of this work was to evaluate machine learning methods, binary classification and sequence labeling, for medication-attribute linkage detection in two clinical corpora. DATA AND METHODS We double annotated 3000 clinical trial announcements (CTA) and 1655 clinical notes (CN) for medication named entities and their attributes. A binary support vector machine (SVM) classificati...

In this study, participants categorized stimuli in a oneattribute rule visual search classification paradigm. The stimuli were six-shape displays that included a rule attribute and five diagnostic attributes. In Experiment 1, attribute values were changed at transfer. Slower RTs were obtained when attribute values from conflicting categories were used. In Experiment 2, the rule attribute (and u...

K nearest neighbor classifier (K-NN) is widely discussed and applied in pattern recognition and machine learning, however, as a similar lazy classifier using local information for recognizing a new test, neighborhood classifier, few literatures are reported on. In this paper, we introduce neighborhood rough set model as a uniform framework to understand and implement neighborhood classifiers. T...

Naive Bayes is a simple, computationally efficient and remarkably accurate approach to classification learning. These properties have led to its wide deployment in many online applications. However, it is based on an assumption that all attributes are conditionally independent given the class. This assumption leads to decreased accuracy in some applications. AODE overcomes the attribute indepen...

This paper explores the use of the naive Bayes classifier as the basis for personalised spam filters. Several machine learning algorithms, including variants of naive Bayes, have previously been used for this purpose, but the author’s implementation using wordposition-based attribute vectors gave very good results when tested on several publicly available corpora. The effects of various forms o...