High-speed corner detection based on fuzzy ID3 decision tree

2012 ◽  
Vol 19 (9) ◽  
pp. 2528-2533 ◽  
Author(s):  
Ru-jiao Duan ◽  
Wei Zhao ◽  
Song-ling Huang ◽  
Kuan-sheng Hao
Keyword(s):  
Decision Tree ◽  
High Speed ◽  
Corner Detection

Classifier Ensemble Algorithm for Data Stream with Attribute Uncertainty

2016 ◽  
Vol 13 (10) ◽  
pp. 7519-7525 ◽  
Author(s):  
Zhang Xing ◽  
Wang MeiLi ◽  
Zhang Yang ◽  
Ning Jifeng
Keyword(s):  
Decision Tree ◽  
Data Stream ◽  
High Speed ◽  
Information Gain ◽  
Uncertain Data ◽  
Classifier Ensemble ◽  
Ensemble Classifiers ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Ensemble Algorithm

To build a classifier for uncertain data stream, an Ensemble of Uncertain Decision Tree Algorithm (EDTU) is proposed. Firstly, the decision tree algorithm for uncertain data (DTU) was improved by changing the calculation method of its information gain and improving the efficiency of the algorithm so that it can process the high-speed flow of data streams; then, based on this basic classifier, dynamic classifier ensemble algorithm was used, and the classifiers presenting effective classification were selected to constitute ensemble classifiers. Experimental results on SEA and Forest Covertype Datasets demonstrate that the proposed EDTU algorithm is efficient in classifying data stream with uncertain attribute, and the performance is stable under the different parameters.

scholarly journals High-Speed FIR Filter Design using Decision Tree Algorithm with FPGA Debugging

2020 ◽  
Vol 9 (3) ◽  
pp. 764-770
Keyword(s):  
Decision Tree ◽  
High Speed ◽  
Filter Design ◽  
Fir Filter ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Constant Multiplication ◽  
Field Programmable ◽  
Fir Filter Design ◽  
Complex Multiplier

In recent years, the filter is one of the key elements in signal processing applications to remove unwanted information. However, traditional FIR filters have been consumed more resources due to complex multiplier design. Mostly the complexity of the FIR filter is dominated by multiplier design. The conventional multipliers can be realized by Single Constant Multiplication (SCM) and Multiple Constant Multiplication (MCM) algorithms using shift and add/subtract operations. In this paper, a hybrid state decision tree algorithm is introduced to reduce hardware utilization (area) and increase speed in filter tap cells of FIR. The proposed scheme generates a decision tree to perform shift & addition and accumulation based on the combined SCM/MCM approach. The proposed FIR filter was implemented in Xilinx Field Programmable Gate Array (FPGA) platform by using Verilog language. The experimental results of the DTG-FIR filter were averagely reduced the 48.259% of LUTs, 51.567 % of flip flops and 44.497 % of slices at 183.122 MHz of operating frequency on the Virtex-5 than existing VP-FIR.

scholarly journals A Comparative Study on Drivers’ Stop/Go Behavior at Signalized Intersections Based on Decision Tree Classification Model

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Sheng Dong ◽  
Jibiao Zhou
Keyword(s):  
Decision Making ◽  
Decision Tree ◽  
High Speed ◽  
Signalized Intersections ◽  
Speed Limit ◽  
Vehicle Type ◽  
Decision Tree Classification ◽  
Signal Change ◽  
Signal Period ◽  
Instantaneous Speed

The stop/go decisions at signalized intersections are closely related to driving speed during signal change intervals. The speed during stop/go decision-making has a significant influence on the dilemma area, resulting in changes of stop/go decisions and high complexity of the decision-making process. Considering that traffic delays and vehicle exhaust pollution are mainly caused by queuing at intersections, the stop-line passing speed during the signal change interval will affect both vehicle operation safety and the atmospheric environment. This paper presents a comparative study on drivers’ stop/go behaviors when facing a transition signal period consisting of 3 s green flashing light (FG) and 3 s yellow light (Y) at rural high-speed intersections and urban intersections. For this study, 1,459 high-quality vehicle trajectories of five intersections in Shanghai during the transition signal period were collected. Of these five intersections, three are high-speed intersections with a speed limit of 80 km/h, and the other two are urban intersections with a speed limit of 50 km/h. Trajectory data of these vehicle samples were statistically analyzed to investigate the general characteristics of potential influencing factors, including the instantaneous speed and the distance to the intersection at the start of FG, the vehicle type, and so on. Decision Tree Classification (DTC) models are developed to reveal the relationship between the drivers’ stop/go decisions and these possible influencing factors. The results indicate that the instantaneous speed of FG onset, the distance to the intersection at the start of FG, and the vehicle type are the most important predictors for both types of intersections. Besides, a DTC model can offer a simple way of modeling drivers’ stopping decision behavior and produce good results for urban intersections.

scholarly journals Wildcard Fields-Based Partitioning for Fast and Scalable Packet Classification in Vehicle-to-Everything

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2563 ◽  
Author(s):  
Jaehyung Wee ◽  
Jin-Ghoo Choi ◽  
Wooguil Pak
Keyword(s):  
Decision Tree ◽  
Communication Networks ◽  
High Speed ◽  
Classification Performance ◽  
Packet Classification ◽  
Classification Algorithms ◽  
Packet Processing ◽  
Security Issues ◽  
Security Of Network ◽  
High Level

Vehicle-to-Everything (V2X) requires high-speed communication and high-level security. However, as the number of connected devices increases exponentially, communication networks are suffering from huge traffic and various security issues. It is well known that performance and security of network equipment significantly depends on the packet classification algorithm because it is one of the most fundamental packet processing functions. Thus, the algorithm should run fast even with the huge set of packet processing rules. Unfortunately, previous packet classification algorithms have focused on the processing speed only, failing to be scalable with the rule-set size. In this paper, we propose a new packet classification approach balancing classification speed and scalability. It can be applied to most decision tree-based packet classification algorithms such as HyperCuts and EffiCuts. It determines partitioning fields considering the rule duplication explicitly, which makes the algorithm memory-effective. In addition, the proposed approach reduces the decision tree size substantially with the minimal sacrifice of classification performance. As a result, we can attain high-speed packet classification and scalability simultaneously, which is very essential for latest services such as V2X and Internet-of-Things (IoT).

Metamorphic Malware Detection Using Minimal Opcode Statistical Patterns

2018 ◽  
pp. 337-359
Author(s):  
Mahmood Fazlali ◽  
Peyman Khodamoradi
Keyword(s):  
Feature Selection ◽  
Decision Tree ◽  
High Speed ◽  
Detection Method ◽  
Malware Detection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Input File ◽  
Feature Vectors ◽  
Metamorphic Malware

High-speed and accurate malware detection for metamorphic malware are two goals in antiviruses. To reach beyond this issue, this chapter presents a new malware detection method that can be summarized as follows: (1) Input file is disassembled and classified to obtain the minimal opcode pattern as feature vectors; (2) a forward feature selection method (i.e., maximum relevancy and minimum redundancy) is applied to remove the redundant as well as irrelevant features; and (3) the process ends by classification through using decision tree. The results indicate the proposed method can effectively detect metamorphic malware in terms of speed, efficiency, and accuracy.

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