remote sensing of wildlife habitats using a multi-scale, object-based,

from large amount of data. We focus the general classification in a secured manner and introduce a privacy-preserving decision tree classifier using C4.5 algorithm. The entire original dataset is replaced by unreal dataset for preserving the privacy via dataset complementation. This novel approach can

Abstract – In this paper, we address Privacy-preserving classification problem in a multi-party sense. We focus the general classification in a secured manner and introduce a Privacy-preserving decision tree classifier using C4.5 algorithm without involving third party. C4.5 algorithm is a software

This paper studies how to build a decision tree classifier under the following scenario: a database is vertically partitioned into two pieces, with one piece owned by Alice and the other piece owned by Bob. Alice and Bob want to build a decision tree classifier based on such a database, but due

Classification for very large datasets has many practical applications in data mining. Techniques such as discretization and dataset sampling can be used to scale up decision tree classifiers to large datasets. Unfortunately, both of these techniques can cause a significant loss in accuracy. We

2004 IEEE. This material is provided with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the products or services of the Purdue Research Foundation/University. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

Abstract not found

and to be of general usage these primitives should be rather building blocks than implementations of specific algorithms. In this paper, we describe primitives for building and applying decision tree classifiers. Based on the analysis of available algorithms and previous work in this area we have identified operations

decision tree classifiers. We present some new optimizations that work with many of these classifiers on both sequential and parallel processors. For ease of explanation, we describe these optimizations mostly in the context of SPRINT, a classifier developed recently for large problems where the training

to incorporating privacy mechanism and allow to hide sensitive pattern or itemset before data mining process is executed. This paper mainly focuses on general classification technique decision tree classifier for preserving privacy. It presents a survey on decision tree learning on various privacy techniques.