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In this work we investigate several issues in order to improve the performance of decision trees. Firstly, we introduced or adopt a new composite splitting criterion aimed to improve classification accuracy. Secondly, we derive a new pruning technique using expert knowledge, which is able to significantly reduce the size of tree without degrading the classification accuracy. Finally, we implemented...
Data mining has been defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data". Clustering is the automated search for group of related observations in a data set. The K-Means method is one of the most commonly used clustering techniques for a variety of applications. This paper proposes a method for making the K-Means algorithm...
Record matching is an essential step in duplicate detection as it identifies records representing same real-world entity. Supervised record matching methods require users to provide training data and therefore cannot be applied for web databases where query results are generated on-the-fly. To overcome the problem, a new record matching method named Unsupervised Duplicate Elimination (UDE) is proposed...
In this paper, we present a practical algorithm to deal with the data specific classification problem when there are datasets with different properties. We proposed to integrate error rate, missing values and expert judgment as factors for determining data specific pruning to form Expert Knowledge Based Pruning (EKBP). We conduct an extensive experimental study on openly available 40 real world datasets...
Gait as a behavioural biometrics has been the subject of recent investigations. One of the unique advantages of human gait is that it can be perceived from a distance. A varied range of research has been undertaken within the field of gait recognition. A gait describes the manner of a person's walking. It can be acquired at a distance and if necessary without consent or knowledge of the subject. Human...
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