today a significant part of available data is saved in text database or text documents. the most important thing is to organize these documents. one way to organize text documents is to classify them. to classify texts is to assign text documents to their actual categories. this has two main steps, i.e. feature- and learning algorithm selection. there have been several methods suggested to clas...

internet applications spreading and its high usage popularity result insignificant increasing of cyber-attacks. consequently, network security has becomea matter of importance and several methods have been developed for these attacks.for this purpose, intrusion detection systems (ids) are being used to monitor theattacks occurred on computer networks. data mining techniques, machinelearning, ne...

background : breast cancer survival has been analyzed by many standard data mining algorithms. a group of these algorithms belonged to the decision tree category. ability of the decision tree algorithms in terms of visualizing and formulating of hidden patterns among study variables were main reasons to apply an algorithm from the decision tree category in the current study that has not studied...

Support vector machines (SVMs) are designed to solve the binary classification problems at beginning, but in real world, there a lot of multiclassification cases. The methods based on SVM mainly divided into direct and indirect methods, which consist multiple classifiers integrated accordance with certain rules form model, most commonly used present. In this paper, an improved algorithm balance...

Decision tree induction has been widely used to generate classifiers from training data through a process of recursively splitting the data space. In the case of training on continuous-valued data, the associated attributes must be discretized in advance or during the learning process. The commonly used method is to partition the attribute range into two or several intervals using single or a s...

We study the decision tree complexity of the discrete Fréchet distance (decision version) under the L1 and L∞ metrics over R. While algorithms for the Euclidean (L2) discrete Fréchet distance were studied extensively, the problem in other metrics such as L1 and L∞ seems to be much less investigated. For the L1 discrete Fréchet distance in R we present a 2d-linear decision tree with depth O(n lo...

There are many methods to prune decision trees, but the idea of cost-sensitive pruning has received much less investigation even though additional flexibility and increased performance can be obtained from this method. In this paper, we introduce a cost-sensitive decision tree pruning algorithm called CC4.5 based on the C4.5 algorithm. This algorithm uses the same method as C4.5 to construct th...