As the number of networked computers grows, intrusion detection is an essential component in keeping networks secure. Various approaches for intrusion detection are currently being in use with each one has its own merits and demerits. This paper presents our work to test and improve the performance of a new class of decision tree c-fuzzy decision tree to detect intrusion. The work also includes...

Intrusion detection involves a lot of tools that are used to identify different types of attacks against computer systems and networks. With the development of network technologies and applications network attacks are greatly increasing both in number and severe. Open source and commercial network intrusion detection tools are not able to predict new type of attacks based on the previous attack...

Decision tree models typically give good classification decisions but poor probability estimates. In many applications, it is important to have good probability estimates as well. This paper introduces a new algorithm, Bagged Lazy Option Trees (B-LOTs), for constructing decision trees and compares it to an alternative, Bagged Probability Estimation Trees (B-PETs). The quality of the class proba...

Since the packet classification algorithm has its great importance and extensive applications, lots of scholars have done tons of researches on the algorithm. This paper presents a new method of a packet classification algorithm based on improved decision tree. In the proposed algorithm, multi-point segmentation and redundancy deletion are adopted to compress the depth of decision tree. A new d...

To find out the evidence of crime-related evidence and association rules among massive data, the classic decision tree algorithms such as ID3 for classification analysis have appeared in related prototype systems. So how to make it more suitable for computer forensics in variable environments becomes a hot issue. When selecting classification attributes, ID3 relies on computation of information...

We consider the randomized decision tree complexity of the recursive 3-majority function. We prove a lower bound of (1/2 − δ) · 2.57143 for the two-sided-error randomized decision tree complexity of evaluating height h formulae with error δ ∈ [0, 1/2). This improves the lower bound of (1 − 2δ)(7/3) given by Jayram, Kumar, and Sivakumar (STOC’03), and the one of (1 − 2δ) · 2.55 given by Leonardo...

In the paper, with the introduction of data mining algorithm of the classification in detail, and then combining the classification algorithm and incremental learning technology, an incremental decision tree algorithm is proposed to solve the problem of incremental learning and analysis the experimental data for this algorithm. The paper used ID3 and C4.5 algorithm for detailed research. Accord...

Ensembles of classification and regression trees remain popular machine learning methods because they define flexible nonparametric models that predict well and are computationally efficient both during training and testing. During induction of decision trees one aims to find predicates that are maximally informative about the prediction target. To select good predicates most approaches estimat...

Certain data is a data whose values are known precisely whereas uncertain data means whose value are not known precisely. But data is always uncertain in real life applications. In data uncertainty attribute value is represented by a set of values. There are two types of attributes in data sets namely, numerical and categorical attributes. Data uncertainty can arise in both numerical and catego...