Decision tree model

About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.

Beyond Physical Connections: Tree Models in Human Pose Estimation

Abstract: Simple tree models for articulated objects prevails in the last decade. However, it is also believed that these simple tree models are not capable of capturing large variations in many scenarios, such as human pose estimation. This paper attempts to address three questions: 1) are simple tree models sufficient? more specifically, 2) how to use tree models effectively in human pose estimation? and 3) how shall we use combined parts together with single parts efficiently? Assuming we have a set of single parts and combined parts, and the goal is to estimate a joint distribution of their locations. We surprisingly find that no latent variables are introduced in the Leeds Sport Dataset (LSP) during learning latent trees for deformable model, which aims at approximating the joint distributions of body part locations using minimal tree structure. This suggests one can straightforwardly use a mixed representation of single and combined parts to approximate their joint distribution in a simple tree model. As such, one only needs to build Visual Categories of the combined parts, and then perform inference on the learned latent tree. Our method outperformed the state of the art on the LSP, both in the scenarios when the training images are from the same dataset and from the PARSE dataset. Experiments on animal images from the VOC challenge further support our findings.

Generating better decision trees

Abstract: A new decision tree learning algorithm called IDX is described. More general than existing algorithms, IDX addresses issues of decision tree quality largely overlooked in the artificial intelligence and machine learning literature. Decision tree size, error rate, and expected classification cost are just a few of the quality measures it can exploit. Furthermore, decision trees of varying quality can be induced simply by adjusting the complexity of the algorithm. Quality should be addressed during decision tree construction since retrospective pruning of the tree, or of a derived rule set, may be unable to compensate for inferior splitting decisions. The complexity of the algorithm, the basis for the heuristic it embodies, and the results of three different sets of experiments are described.

Hybrid Flexible Neural-Tree-Based Intrusion Detection Systems

Abstract: An intrusion is defined as a violation of the security policy of the system, and, hence, intrusion detection mainly refers to the mechanisms that are developed to detect violations of system security policy. Current intrusion detection systems ~IDS! examine all data features to detect intrusion or misuse patterns. Some of the features may be redundant or contribute little ~if anything! to the detection process. The purpose of this study is to identify important input features in building an IDS that is computationally efficient and effective. This article proposes an IDS model based on a general and enhanced flexible neural tree ~FNT!. Based on the predefined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, overlayer connections, and different activation functions for the various nodes involved. The FNT structure is developed using an evolutionary algorithm, and the parameters are optimized by a particle swarm optimization algorithm. Empirical results indicate that the proposed method is efficient. © 2007 Wiley Periodicals, Inc.