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.

Environmental factors prediction in preterm birth using comparison between logistic regression and decision tree methods: an exploratory analysis

Abstract: Objective The main objective of this paper is to compare the performance of logistic regression and decision tree classification methods and to find the significant environment determinants that causes pre-term birth. Design, setting and population Between 2017 to 2018, 90 pregnant females underwent birth outcome followed by research staff at our institutions, out of those 50 are full-term and 40 are preterm births in this study. Method Before and after feature selection logistic regression and decision tree classifier model has been compared in this dataset and to evaluate the model accuracy. Main outcome measures Preforming the accuracy of machine learning classification model and important factors on pre-term birth. Results: Using chi-square test and find the Area of residence and GSH, MDA, α-HCH, total HCH and total DDT are responsible for the preterm birth. Using the multiple logistic regression, pre term birth was associated with MDA and α-HCH (95% CI 0.04 to 0.48 and 95% CI 0.82 to 0.97). The logistic and decision tree model comparison result shows that logistic regression is better in terms of metrics (precision = 0.92, F1-score = 0.96 and AUROC = 0.97), while decision tree performs the poor (precision = 0.75, F1-score = 0.86 and AUROC = 0.87). Conclusions The logistic regression is accurate model to predict the pre-term as compare to decision tree method. The variables like α-HCH , total HCH and MDA (Malondialdehyde) are the most influential factors for preterm birth.

On the Computational Complexity of Maintaining GPS Clock and Providing Tight Delay Bounds in Packet Schedule

Abstract: Packet scheduling is an important mechanism for providing QoS guarantees in data networks. A scheduling algorithm in general consists of two functions: one estimates how the GPS (General Processor Sharing) clock progresses with respect to the real time, and the other decides the order of serving the packets based on the estimation of their GPS start/finish times. In this work, we answer important open questions concerning the computational complexity of performing both functions. The first part of our work systematically studies the complexity of computing the GPS virtual start/finish times of the packets, which is long believed to be per packet but has never been proved or explicitly refuted. It also answers several other related open questions such as “whether the complexity can be lower if we only want to compute the relative order of the GPS finish times of the packets rather than their exact values?” The second part of our work studies the inherent complexity for scheduling algorithms to guarantee tight delay bounds. We extend the prior work by Xu and Lipton to a stronger and more practical computational model and explore related issues. We show rigorously that existing methodologies used in prior work will not be suitable for establishing lower bound results under the new model.

Reduced complexity for the radius adaptive K-best algorithm

Abstract: The detection of multiple-input multiple-output (MIMO) system is an important issue. The radius adaptive K-Best (RAKB) algorithm is proposed for the detection of the MIMO system, it decomposes the searching tree into several subbranches and provides similar bit-error-rate (BER) performance to the K-Best algorithm. But the complexity of the RAKB algorithm is still very high, it will pay an additional arithmetic at low signal-to-noise ratio (SNR). In this paper, we improve the RAKB algorithm to simply the complexity. The improved algorithm only searches the preinstall sub-branch at low SNR. Simulation results show that the improved algorithm achieves similar performance to the K-Best algorithm with a significantly reduced complexity, in terms of the number of visited nodes and computational time.

Intrusion Detection and Prevention System in Cloud using Decision Tree Model

Abstract: Distributed and open structure of Cloud Computing model and its services makes it attractive for potential intruders. Providing security in a distributed system requires more than user authentication with passwords or digital certificates and confidentiality in data transmission. Distributed model of Cloud makes it vulnerable and prone to sophisticated distributed intrusion attacks like Distributed Denial of Service (DDOS). The conventional Intrusion detection and prevention systems are not sufficient to be deployed in Cloud environment because of its openness and service structures. The objective of this project is to analyze or investigate possible solutions to detect and prevent intrusions in Cloud Computing Systems. Based on the survey and experiences with traditional intrusion detection systems, decision tree based models is proposed for IDPS implementation.