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Conference on Automatic Target Recognition XXIV

作者： Sokolnikov, Andre Visual Solut & Applicat South Bend IN USA

ISBN: (纸本)9781628410273

If we assume that a natural image can be modeled as a succession of a multilevel system, we can develop an optimal routine of a matrix superposition. Each matrix separates the fundamental elements by a set of optimal criteria. The matrix superposition is then characterized by a tree-based principle which is applied adaptively. We will also demonstrate how the missing data constrains may be overcome by collecting additional measurements.

关键词： Automatic Target Recognition adaptive modeling prediction modeling tree-based algorithm

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SurvReLU: Inherently Interpretable Survival Analysis via Deep ReLU Networks 24

SurvReLU: Inherently Interpretable Survival Analysis via Dee...

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33rd ACM International Conference on Information and Knowledge Management (CIKM)

作者： Sun, Xiaotong Qiu, Peijie Zhang, Shengfan Univ Arkansas Fayetteville AR 72701 USA Washington Univ St Louis St Louis MO USA

ISBN: (纸本)9798400704369

Survival analysis models time-to-event distributions with censorship. Recently, deep survival models using neural networks have dominated due to their representational power and state-of-the-art performance. However, their "black-box" nature hinders interpretability, which is crucial in real-world applications. In contrast, "white-box" tree-based survival models offer better interpretability but struggle to converge to global optima due to greedy expansion. In this paper, we bridge the gap between previous deep survival models and traditional tree-based survival models through deep rectified linear unit (ReLU) networks. We show that a deliberately constructed deep ReLU network (termed SurvReLU) can harness the interpretability of tree-based structures with the representational power of deep survival models. Empirical studies on both simulated and real survival benchmark datasets showed the effectiveness of the proposed SurvReLU in terms of performance and interoperability. The code is available at https://***/xs018/SurvReLU.

关键词： Survival analysis Interpretability tree-based algorithm

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Multisensor fusion with the ramification algorithm 25

Multisensor fusion with the ramification algorithm

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Conference on Automatic Target Recognition XXV

作者： Sokolnikov, Andre Visual Solut & Applicat South Bend IN 46615 USA

The present approach combines data fusion from several sensor types to enhance the overall detection and classification performance. The fusion of different sensors is implemented at data and feature levels that resul... 详细信息

ISBN: (纸本)9781628415926

关键词： Automatic Target Recognition adaptive modeling prediction modeling tree-based algorithm multisensor fusion ramification algorithm

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New Methods for Disparity Estimation

New Methods for Disparity Estimation

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作者： Yu, Huilin University of Miami

学位级别：Ph.D.

Health disparity is an important public health policy concern as it is related to the social inequalities in population health, not only for the persons experiencing them, but also for the entire population in the society. People experiencing poorer health status will negatively impact the overall health of the nation and this inequality is costly as well as burdensome to healthcare system. Health disparities studies allow us to identify and understand disparities, eventually design intervention strategies that could be more effective in reducing disparities. This thesis is mainly focused on building new statistical models for disparity studies in response to the complex data types which we encounter today. We propose tree-based models to unveil the distribution of disparities in a population through the hierarchical interaction between individual level variables (like clinical variables or genetic variables) and social determinants of health (like SES, education level etc). Precision medicine has the potential to revolutionize medicine because clinical decisions can in theory be made in a manner that is more customized to an individual patient. It’s not surprising then that there has been growing interest in trying to identify and reduce disparities using precision medicine constructs. Central to this paradigm is the search for what we term disparity subtypes. We will take the tree-based models we developed as well as another framework known as peeling and develop a new statistical framework for the identification of disparity subtypes. Even though much of disparity science has traditionally focused on social determinants of health, the move towards an integrative framework together with biological determinants requires that researchers must be able to find a common language and framework for connecting the two. Additionally, as biological data increases, so does the contextual information that researchers are collecting. Thus it’s imperative to be able to quantify

关键词： disparity estimation tree-based algorithm individual and environmental factor interaction

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Fast Winding Numbers for Soups and Clouds

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ACM TRANSACTIONS ON GRAPHICS 2018年第4期37卷 1-12页

作者： Barill, Gavin Dickson, Neil G. Schmidt, Ryan Levin, David I. W. Jacobson, Alec Univ Toronto Toronto ON Canada Side Effects Software Inc Toronto ON Canada Gradientspace Toronto ON Canada UofT 40 St George St Toronto ON M5S 2E4 Canada

Inside-outside determination is a basic building block for higher-level geometry processing operations. Generalized winding numbers provide a robust answer for triangle meshes, regardless of defects such as self-intersections, holes or degeneracies. In this paper, we further generalize the winding number to point clouds. Previous methods for evaluating the winding number are slow for completely disconnected surfaces, such as triangle soups or-in the extreme case-point clouds. We propose a tree-based algorithm to reduce the asymptotic complexity of generalized winding number computation, while closely approximating the exact value. Armed with a fast evaluation, we demonstrate the winding number in a variety of new applications: voxelization, signing distances, generating 3D printer paths, defect-tolerant mesh booleans and point set surfaces.

关键词： generalized winding number inside-outside segmentation tree-based algorithm robust geometry processing

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LRF : A logically randomized forest algorithm for classification and regression problems

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartC期213卷

作者： Jain, Nishant Jana, Prasanta K. Indian Inst Technol Indian Sch Mines Dept Comp Sci & Engn Dhanbad India

tree-based ensemble algorithms (TEAs) have made significant advances in recent years due to their simple algorithmic design. However, when the proportion of the 'most informative' features is low, the performance of conventional TEAs degrades significantly. The primary rationale for performance degradation is that traditional algorithmic design appears to be biased toward the least informative features, and the sub-space selection procedure contains uninformative features. This paper proposes a logically randomized forest (LRF) algorithm by incorporating two different enhancements into existing TEAs. The first enhancement is made to address the issue of biasness by performing feature-level engineering. The second enhancement is the approach by which individual feature sub-spaces are selected. To derive the first enhancement, we use the graph-theoretic principle of minimal vertex cover to construct a relevant assemblage of features. Following that, the permutation-based feature importance technique is employed to calculate the 'informativeness' of the relevant features in order to infuse logical randomness into the individual trees in the forest. For the second enhancement, the stratified sampling method is used to ensure that the most informative features are present in all newly created feature sub-spaces. Consequently, individual trees are generated using the Roulette wheel-based selection (RWS) algorithm. The proposed algorithm has been evaluated on two real -world genomic data sets, ten hybrid-synthetic classification data sets, and twenty multidisciplinary benchmark data sets with varying characteristics. The experimental findings demonstrate that the LRF outperforms the existing benchmark and cutting-edge TEAs.

关键词： tree-based algorithm Logical randomness Random forest Ensemble learning Decision tree

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Predicting pulsar stars using a random tree boosting voting classifier (RTB-VC)

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ASTRONOMY AND COMPUTING 2020年 32卷

作者： Rustam, F. Mehmood, A. Ullah, S. Ahmad, M. Khan, D. Muhammad Choi, G. S. On, B-W Khwaja Fareed Univ Engn & Informat Technol Dept Comp Sci Rahim Yar Khan 64200 Punjab Pakistan Islamia Univ Bahawalpur Dept Comp Sci & Informat Technol Punjab 63100 Pakistan Yeungnam Univ Dept Informat & Commun Engn Gyeongbuk 38541 South Korea Kunsan Natl Univ Dept Software Convergence Engn Gunsan 54150 South Korea

The recent exponential growth in the data volume and number of identified pulsar stars is due to pulsar candidate search experiments and surveys. In this study, we investigated the existing methods and techniques used for pulsar prediction, such as applying filters based on pulsar observations, which can adversely affect the success of accurate pulsar prediction. Some of the existing methods are not capable of dealing with large volumes of data and others fail to accurately select the best candidates from pulsar observations. Thus, we developed a new approach based on the traditional supervised machine learning algorithm, which yields faster and more accurate results. In this study, we present our hybrid machine learning classifier called the random trees boosting voting classifier (RTB-VC) for predicting pulsar stars. RTB-VC combines tree-based classifiers and it employs the High Time Resolution Universe 2 (HTRU2) data set comprising a set of eight features related to pulsars and nonpulsars. The HTRU2 data set is imbalanced and we solve this problem by using the synthetic minority oversampling technique to generate artificial data and obtain a balanced data set. A feature set is used to separate pulsar and non-pulsar candidates because the different distributions of variables in the data set are helpful for training models. In the proposed approach, the prediction stage of RTB-VC is based on a combination of soft voting, hard voting, and weighted voting to obtain highly accurate and relevant criteria for finally predicting pulsars or non-pulsars. The ensemble-based structure of RTB-VC yields accurate results based on pulsar observations with a high F-1 score for pulsars (98.3%). We evaluated the learning algorithm in terms of its accuracy, precision, recall, and F-1 score. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Pulsar stars Astronomy Machine learning Ensemble learning tree-based algorithm SMOTE

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algorithmic Solutions for RFID Tag Anti-Collision Problem in Supply Chain Management

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Procedia Computer Science 2019年 151卷 929-934页

作者： Kamalendu Pal Department of Computer Science City University of London London ECV 0HB United Kingdom

The Radio Frequency IDentification (RFID) is a wireless communication technology that enables automatic identification, tracking and data collection from any tagged object in a supply chain operating environment. A simple RFID system uses radio signals to transmit data via a tiny portable device, called a tag, which is read by an RFID reader and processed by the corporate information system (IS) to meet the needs of business management. One of the important performance issues in this system is to resolve RFID tag collision. Tag collision happens when two or more tags reflects-back their individual identification radio signals to the reader at the same time thus confusing the reader identification process. Different algorithmic solutions on tag collision are available. They are generally in two main categories of anti-collision problems: ALOHA-based solutions and tree-based solutions. However, ALOHA-based algorithms suffer from a time-related starvation problem. The well-known tree-based algorithms are Binary tree (BT) and Query tree (QT). In addition, QT algorithms are very efficient in memory utilization in comparison to other algorithmic solutions. This paper presents simulation-based experimental results on the performance of some well-known BT-based algorithms: simple Binary Search algorithm (BSA), Dynamic Binary Search algorithm (DBSA), and Backtrack Binary algorithm (BBA).

关键词： Radio Frequency Identification Wireless Communication Supply Chain Management Tag Collision ALOHA-based algorithm tree-based algorithm

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RFID Anti-Collision Technology in Big Data Environment

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INTERNATIONAL JOURNAL OF ONLINE ENGINEERING 2018年第5期14卷 42-57页

作者： Chen, Buying Xingtai Polytech Coll Informat Engn Dept Xingtai Peoples R China

Along with the rapid development of Web of Things, the RFID technology is widely applied in every field, but today, the great challenge we face is how to avoid information conflict and collision in the process of acquisition and treatment of massive information. It is the keystone of the study. This paper conducts a comparative analysis on different tree-based algorithms as improved, and integrated with multiple sub-cycle response mechanism, a Multi-Response Collision tree algorithm is proposed. And beyond that, this paper simulates and analyzed this algorithm and other improved ones. The results reveal that, compared to other algorithms, MRCT algorithm features a better performance, less recognition cycles, least query time slots on average, and ceiling throughput rate.

关键词： RFID anti-collision tree-based algorithm Manchester code

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