Text categorization plays a crucial role in both academic and commercial platforms due to the growing demand for automatic organization of documents. Kernel-based classification algorithms such as Support Vector Machines (SVM) have become highly popular in the task of text mining. This is mainly due to their relatively high classification accuracy on several application domains as well as their ability to handle high dimensional and sparse data which is the prohibitive characteristics of textual data representation. Recently, there is an increased interest in the exploitation of background knowledge such as ontologies and corpus-based statistical knowledge in text categorization. It has been shown that, by replacing the standard kernel functions such as linear kernel with customized kernel functions which take advantage of this background knowledge, it is possible to increase the performance of SVM in the text classification domain. Based on this, we propose a novel semantic smoothing kernel for SVM. The suggested approach is based on a meaning measure, which calculates the meaningfulness of the terms in the context of classes. The documents vectors are smoothed based on these meaning values of the terms in the context of classes. Since we efficiently make use of the class information in the smoothing process, it can be considered a supervised smoothing kernel. The meaning measure is based on the Helmholtz principle from Gestalt theory and has previously been applied to several text mining applications such as document summarization and feature extraction. However, to the best of our knowledge, ours is the first study to use meaning measure in a supervised setting to build a semantic kernel for SVM. We evaluated the proposed approach by conducting a large number of experiments on well-known textual datasets and present results with respect to different experimental conditions. We compare our results with traditional kernels used in SVM such as linear kernel as well as with several corpus-based semantic kernels. Our results show that classification performance of the proposed approach outperforms other kernels

The underlying assumption in traditional machine learning algorithms is that instances are Independent and Identically Distributed (IID). These critical independence assumptions made in traditional machine learning algorithms prevent them from going beyond instance boundaries to exploit latent relations between features. In this paper, we develop a general approach to supervised learning by leveraging higher order dependencies between features. We introduce a novel Bayesian framework for classification termed Higher Order Naïve Bayes (HONB). Unlike approaches that assume data instances are independent, HONB leverages higher order relations between features across different instances. The approach is validated in the classification domain on widely used benchmark data sets. Results obtained on several benchmark text corpora demonstrate that higher order approaches achieve significant improvements in classification accuracy over the baseline methods, especially when training data is scarce. A complexity analysis also reveals that the space and time complexity of HONB compare favorably with existing approaches.

Abnormal events such as large scale power outages, misconfigurations, and worm attacks can affect the global routing infrastructure and consequently create regional or global Internet service interruptions. As a result, early detection of abnormal events is of critical importance. In this study we present a framework based on data mining algorithms that are applied to anomaly detection on global routing infrastructure. To show the applicability of our framework, we conduct extensive experiments with a variety of abnormal events and classification algorithms. Our results demonstrate that when we train our system with abnormal events including worm attacks, power supply outages, submarine cable cuts, and misconfigurations, we can detect a similar type of event as it happens.

Preprocessing is an important task and critical step in information retrieval and text mining. The objective of this study is to analyze the effect of preprocessing methods in text classification on Turkish texts. We compiled two large datasets from Turkish newspapers using a crawler. On these compiled data sets and using two additional datasets, we perform a detailed analysis of preprocessing methods such as stemming, stopword filtering and word weighting for Turkish text classification on several different Turkish datasets. We report the results of extensive experiments.