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.

Route/Site Selection of Urban Transportation Facilities: An Integrated GIS/MCDM Approach

Abstract: Route/site selection is the process of finding locations that meet desired conditions set by the selection criteria. In such a process, manipulation of spatial data and satisfaction of multiple criteria are essential to the success of decision-making. Because of the complexity of the problems a number of tools must be deployed to arrive at the proper solution. Expert systems, geographic information systems and multi-criteria decision making techniques have been systematically used for decades to support such projects. This paper discusses the most recent developments of this field. A hierarchical decision tree model is prepared to join the diverse engineering, economical, institutional and social perspectives as well as the environmental objectives. A comprehensive example of the route/site selection process of a metro-rail network project is also presented.

Method and apparatus for visualizing and interacting with decision trees

Abstract: A decision tree model is generated from sample data. A visualization system may automatically prune the decision tree model based on characteristics of nodes or branches in the decision tree or based on artifacts associated with model generation. For example, only nodes or questions in the decision tree receiving a largest amount of the sample data may be displayed in the decision tree. The nodes also may be displayed in a manner to more readily identify associated fields or metrics. For example, the nodes may be displayed in different colors and the colors may be associated with different node questions or answers.

Improving activity recognition via automatic decision tree pruning

Abstract: Activity recognition enables many user-facing smartphone applications, but it may suffer from misclassifications when trained models attempt to classify previously-unseen real-world behavior. Our system mitigates this problem by first identifying spurious classifications and then automatically pruning a decision tree model to remove labels that tend to produce wrong inferences, resulting in a 10% classification improvement based on our data set.

BehavDT: A Behavioral Decision Tree Learning to Build User-Centric Context-Aware Predictive Model

Abstract: This paper formulates the problem of building a context-aware predictive model based on user diverse behavioral activities with smartphones. In the area of machine learning and data science, a tree-like model as that of decision tree is considered as one of the most popular classification techniques, which can be used to build a data-driven predictive model. The traditional decision tree model typically creates a number of leaf nodes as decision nodes that represent context-specific rigid decisions, and consequently may cause overfitting problem in behavior modeling. However, in many practical scenarios within the context-aware environment, the generalized outcomes could play an important role to effectively capture user behavior. In this paper, we propose a behavioral decision tree, "BehavDT" context-aware model that takes into account user behavior-oriented generalization according to individual preference level. The BehavDT model outputs not only the generalized decisions but also the context-specific decisions in relevant exceptional cases. The effectiveness of our BehavDT model is studied by conducting experiments on individual user real smartphone datasets. Our experimental results show that the proposed BehavDT context-aware model is more effective when compared with the traditional machine learning approaches, in predicting user diverse behaviors considering multi-dimensional contexts.