3,745 research outputs found
Cost-Sensitive Decision Tree with Multiple Resource Constraints
Resource constraints are commonly found in classification tasks. For example, there could be a budget limit on implementation and a deadline for finishing the classification task. Applying the top-down approach for tree induction in this situation may have significant drawbacks. In particular, it is difficult, especially in an early stage of tree induction, to assess an attribute’s contribution to improving the total implementation cost and its impact on attribute selection in later stages because of the deadline constraint. To address this problem, we propose an innovative algorithm, namely, the Cost-Sensitive Associative Tree (CAT) algorithm. Essentially, the algorithm first extracts and retains association classification rules from the training data which satisfy resource constraints, and then uses the rules to construct the final decision tree. The approach has advantages over the traditional top-down approach, first because only feasible classification rules are considered in the tree induction and, second, because their costs and resource use are known. In contrast, in the top-down approach, the information is not available for selecting splitting attributes. The experiment results show that the CAT algorithm significantly outperforms the top-down approach and adapts very well to available resources.Cost-sensitive learning, mining methods and algorithms, decision trees
3D Reconstruction from IR Thermal Images and Reprojective Evaluations
Infrared thermography has been widely used in various domains to measure the temperature distributions of objects and surfaces. The methodology can be further extended to 3D applications if the spatial information of the temperature distribution is available. This paper proposes a 3D infrared imaging approach based on silhouette volume intersection to reconstruct volumetric temperature data of enclosed objects. 3D IR images are taken from various angles and integrated with 2D RGB images to effectively reconstruct a 3D model of the object's temperature distributions. Various automatic thresholding methods are also compared and evaluated by reprojection scoring to systematically assess the effectiveness and accuracy of the different approaches. Experiment results have demonstrated the ability of the system to provide an estimate to the 3D location of an internal heat source from images taken externally
Profit Maximization by Forming Federations of Geo-Distributed MEC Platforms
This paper has been presented at: Seventh International Workshop on Cloud Technologies and Energy Efficiency in Mobile Communication Networks (CLEEN 2019). How cloudy and green will mobile network and services be? 15 April 2019 - Marrakech, MoroccoIn press / En prensaMulti-access edge computing (MEC) as an emerging
technology which provides cloud service in the edge of multi-radio
access networks aims to reduce the service latency experienced
by end devices. When individual MEC systems do not have
adequate resource capacity to fulfill service requests, forming
MEC federations for resource sharing could provide economic
incentive to MEC operators. To this end, we need to maximize
social welfare in each federation, which involves efficient federation
structure generations, federation profit maximization by
resource provisioning configuration, and fair profit distribution
among participants. We model the problem as a coalition game
with difference from prior work in the assumption of latency
and locality constraints and also in the consideration of various
service policies/demand preferences. Simulation results show that
the proposed approach always increases profits. If local requests
are served with local resource with priority, federation improves
profits without sacrificing request acceptance rates.This work was partially supported by the Ministry of Science and Technology, Taiwan, under grant numbers 106-2221-E-009-004 and by the H2020 collaborative Europe/Taiwan
research project 5G-CORAL (grant number 761586)
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