This paper work is focused on the comparison of different data mining techniques and their performances by building predictive models of forest stand properties from satellite images. We used the WEKA data mining environment to implement our numeric prediction experiments, applying linear regression, model (regression) trees, and bagging. The best results (with regard to correlation) we obtaine...

In this paper, we propose a classification based approach to the morphological disambiguation for Turkish language. Due to complex morphology in Turkish, any word can get unlimited number of affixes resulting very large tag sets. The problem is defined as choosing one of parses of a word not taking the existing root word into consideration. We trained our model with well-known classifiers using...

Anomalies in computer networks has increased in the last decades and raised concern to create techniques to identify the unusual traffic patterns. This research aims to use data mining techniques in order to correctly identify these anomalies. Weka is a collection of machine learning algorithms for data mining tasks which was used to identify and analyze the anomalies of a data set called SPAMB...

Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Naı̈ve Bayes classifiers at the leaves. MOA supports bi-directi...

Rule extraction from neural networks often focusses on exact equivalence and is often tested on relatively small canonical examples. We apply genetic algorithms to the extract approximate rules from neural networks. The method is robust and works with large networks. We compare the results with rules obtained using state of the art decision tree methods and achieve superior performance to strai...