Data Mining - Concepts and Techniques.

Statistical Analysis with Missing Data

An Introduction to MultiAgent Systems.

A systematic study of the class imbalance problem in convolutional neural networks

Feature selection in machine learning: A new perspective

As an important preprocessing technology in text classification, feature selection can improve the scalability, efficiency and accuracy of a text classifier. In general, a good feature selection method should consider domain and algorithm characteristics. As the Naive Bayesian classifier is very simple and efficient and highly sensitive to feature selection, so the research of feature selection specially for it is significant. This paper presents two feature evaluation metrics for the Naive Bayesian classifier applied on multi-class text datasets: Multi-class Odds Ratio (MOR), and Class Discriminating Measure (CDM). Experiments of text classification with Naive Bayesian classifiers were carried out on two multi-class texts collections. As the results indicate, CDM and MOR gain obviously better selecting effect than other feature selection approaches.

Feature selection for text classification with Naïve Bayes

With the development of the web, large numbers of documents are available on the Internet. Digital libraries, news sources and inner data of companies surge more and more. Automatic text categorization becomes more and more important for dealing with massive data. However the major problem of text categorization is the high dimensionality of the feature space. At present there are many methods to deal with text feature selection. To improve the performance of text categorization, we present another method of dealing with text feature selection. Our study is based on Gini index theory and we design a novel Gini index algorithm to reduce the high dimensionality of the feature space. A new measure function of Gini index is constructed and made to fit text categorization. The results of experiments show that our improvements of Gini index behave better than other methods of feature selection.

http://ccc.inaoep.mx/~villasen/bib/A%20novel%20feature%20selection%20algorithm%20for%20text%20categorization.pdf

A novel feature selection algorithm for text categorization

This paper describes the feature selection method TFIDF (term frequency, inverse document frequency). With it, we process the data resource and set up the vector space model in order to provide a convenient data structure for text categorization. We calculate the precision of this method with the help of categorization results. According to the empirical results, we analyze its advantages and disadvantages and present a new TFIDF-based feature selection approach to improve its accuracy.

Improved feature selection approach TFIDF in text mining

With the tremendous growth of the Internet, information system security has become an issue of serious global concern due to the rapid connection and accessibility. Developing effective methods for intrusion detection, therefore, is an urgent task for assuring computer & information system security. Since most attacks and misuses can be recognized through the examination of system audit log files and pattern analysis therein, an approach for intrusion detection can be built on them. First we have made deep analysis on attacks and misuses patterns in log files; and then proposed an approach using support vector machines for anomaly detection. It is a one-class SVM based approach, trained with abstracted user audit logs data from 1999 DARPA.

Houkuan Huang

Papers

Feature selection for text classification with Naïve Bayes

A novel feature selection algorithm for text categorization

Improved feature selection approach TFIDF in text mining

Improving one-class SVM for anomaly detection

An improved NEH-based heuristic for the permutation flowshop problem