Missing Data Prediction using Correlation Genetic Algorithm and SVM Approach

Alert correlation and prediction using data mining and HMM

Intrusion Detection Systems (IDSs) are security tools widely used in computer networks. While they seem to be promising technologies, they pose some serious drawbacks: When utilized in large and high traffic networks, IDSs generate high volumes of low-level alerts which are hardly manageable. Accordingly, there emerged a recent track of security research, focused on alert correlation, which ext...

A Genetic Algorithm Approach for Non-Ignorable Missing Data

The databases store data that may be subjected to missing values either in data acquisition or data storage process. The proposed approach uses the widely used optimization technique called genetic algorithm for the NMAR (Not Missing At Random) missing mechanism which prevails more in real life that are non-ignorable. Since the non-ignorable mechanism needs prior

A Genetic Algorithm Approach for Non-Ignorable Missing Data

The databases store data that may be subjected to missing values either in data acquisition or data storage process. The proposed approach uses the widely used optimization technique called genetic algorithm for the NMAR (Not Missing At Random) missing mechanism which prevails more in real life that are nonignorable. Since the non-ignorable mechanism needs prior basic knowledge about the data t...

DEA with Missing Data: An Interval Data Assignment Approach

In the classical data envelopment analysis (DEA) models, inputs and outputs are assumed as known variables, and these models cannot deal with unknown amounts of variables directly. In recent years, there are few researches on handling missing data. This paper suggests a new interval based approach to apply missing data, which is the modified version of Kousmanen (2009) approach. First, the prop...

Material Characterization of Structures Using Genetic Algorithm Vibetarional Data