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Analyzing of incremental high utility pattern mining based on tree structures

HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2017年第1期7卷 1页

作者： Lee, Judae Yun, Unil Lee, Gangin Sejong Univ Dept Comp Engn Seoul South Korea

Since the concept of high utility pattern mining was proposed to solve the drawbacks of traditional frequent pattern mining approach that cannot handle various features of real-world applications, many different techniques and algorithms for high utility pattern mining have been developed. Moreover, several advanced methods for incremental data processing have been proposed in recent years as the sizes of recent databases obtained in the real world become larger. In this paper, we introduce the basic concept of incremental high utility pattern mining and analyze various relevant methods. In addition, we also conduct performance evaluation for the methods with famous benchmark datasets in order to determine their detailed characteristics. The evaluation shows that the less candidate patterns make algorithms faster.

关键词： Data mining Pattern mining Utility mining Incremental mining High utility patterns tree-based algorithms

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Principal Curve algorithms for Partitioning High-Dimensional Data Spaces

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2011年第3期22卷 367-380页

作者： Zhang, Junping Wang, Xiaodan Kruger, Uwe Wang, Fei-Yue Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai 200433 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Petr Inst Dept Chem Engn Abu Dhabi 2533 U Arab Emirates Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Univ Arizona Tucson AZ 85719 USA

Most partitioning algorithms iteratively partition a space into cells that contain underlying linear or nonlinear structures using linear partitioning strategies. The compactness of each cell depends on how well the (locally) linear partitioning strategy approximates the intrinsic structure. To partition a compact structure for complex data in a nonlinear context, this paper proposes a nonlinear partition strategy. This is a principal curve tree (PC-tree), which is implemented iteratively. Given that a PC passes through the middle of the data distribution, it allows for partitioning based on the arc length of the PC. To enhance the partitioning of a given space, a residual version of the PC-tree algorithm is developed, denoted here as the principal component analysis tree (PCR-tree) algorithm. Because of its residual property, the PCR-tree can yield the intrinsic dimension of high-dimensional data. Comparisons presented in this paper confirm that the proposed PC-tree and PCR-tree approaches show a better performance than several other competing partitioning algorithms in terms of vector quantization error and nearest neighbor search. The comparison also shows that the proposed algorithms outperform competing linear methods in total average coverage which measures the nonlinear compactness of partitioning algorithms.

关键词： Manifold learning principal component analysis principal curves space partitioning tree-based algorithms

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The expected bit complexity of the von Neumann rejection algorithm

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STATISTICS AND COMPUTING 2017年第3期27卷 699-710页

作者： Devroye, Luc Gravel, Claude McGill Univ Sch Comp Sci Montreal PQ Canada Univ Montreal Dept Comp Sci & Operat Res Montreal PQ Canada

In 1952, von Neumann introduced the rejection method for random variate generation. We revisit this algorithm when we have a source of perfect bits at our disposal. In this random bit model, there are universal lower bounds for generating a random variate with a given density to within an accuracy derived by Knuth and Yao, and refined by the authors. In general, von Neumann's method fails in this model. We propose a modification that insures proper behavior for all Riemann-integrable densities on compact sets, and show that the expected number of random bits needed behaves optimally with respect to universal lower bounds. In particular, we introduce the notion of an oracle that evaluates the supremum and infimum of a function on any rectangle of , and develop a quadtree-style extension of the classical rejection method.

关键词： Random number generation Random bit model Von Neumann sampling algorithm tree-based algorithms Random sampling Entropy

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CLASSIFICATION algorithms USING ADAPTIVE PARTITIONING

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ANNALS OF STATISTICS 2014年第6期42卷 2141-2163页

作者： Binev, Peter Cohen, Albert Dahmen, Wolfgang DeVore, Ronald Univ S Carolina Dept Math Columbia SC 29208 USA Univ Paris 06 UMR 7598 Lab Jacques Louis Lions F-75005 Paris France Rhein Westfal TH Aachen Inst Geometrie & Prakt Math D-52056 Aachen Germany Texas A&M Univ Dept Math College Stn TX 77840 USA

algorithms for binary classification based on adaptive tree partitioning are formulated and analyzed for both their risk performance and their friendliness to numerical implementation. The algorithms can be-viewed as generating a set approximation to the Bayes set and thus fall into the general category of set estimators. In contrast with the most studied tree-based algorithms, which utilize piecewise constant approximation on the generated partition [IEEE Trans. Inform. Theory 52 (2006) 1335-1353;Mach. Learn. 66 (2007) 209-242], we consider decorated trees, which allow us to derive higher order methods. Convergence rates for these methods are derived in terms the parameter alpha of margin conditions and a rate s of best approximation of the Bayes set by decorated adaptive partitions. They can also be expressed in terms of the Besov smoothness beta of the regression function that governs its approximability by piecewise polynomials on adaptive partition. The execution of the algorithms does not require knowledge of the smoothness or margin conditions. Besov smoothness conditions are weaker than the commonly used Holder conditions, which govern approximation by nonadaptive partitions, and therefore for a given regression function can result in a higher rate of convergence. This in turn mitigates the compatibility conflict between smoothness and margin parameters.

关键词： Binary classification adaptive methods set estimators tree-based algorithms

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TACOS: a novel approach for accurate prediction of cell-specific long noncoding RNAs subcellular localization

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BRIEFINGS IN BIOINFORMATICS 2022年第4期23卷 bbac243-bbac243页

作者： Jeon, Young-Jun Hasan, Md Mehedi Park, Hyun Woo Lee, Ki Wook Manavalan, Balachandran Sungkyunkwan Univ Dept Integrat Biotechnol Suwon South Korea Tulane Univ Tulane Ctr Aging New Orleans LA 70118 USA Tulane Univ Dept Med New Orleans LA 70118 USA Sungkyunkwan Univ Suwon South Korea

Long noncoding RNAs (lncRNAs) are primarily regulated by their cellular localization, which is responsible for their molecular functions, including cell cycle regulation and genome rearrangements. Accurately identifying the subcellular location of lncRNAs from sequence information is crucial for a better understanding of their biological functions and mechanisms. In contrast to traditional experimental methods, bioinformatics or computational methods can be applied for the annotation of lncRNA subcellular locations in humans more effectively. In the past, several machine learning-based methods have been developed to identify lncRNA subcellular localization, but relevant work for identifying cell-specific localization of human lncRNA remains limited. In this study, we present the first application of the tree-based stacking approach, TACOS, which allows users to identify the subcellular localization of human lncRNA in 10 different cell types. Specifically, we conducted comprehensive evaluations of six tree-based classifiers with 10 different feature descriptors, using a newly constructed balanced training dataset for each cell type. Subsequently, the strengths of the AdaBoost baseline models were integrated via a stacking approach, with an appropriate tree-based classifier for the final prediction. TACOS displayed consistent performance in both the cross-validation and independent assessments compared with the other two approaches employed in this study. The user-friendly online TACOS web server can be accessed at https://***/TACOS.

关键词： long noncoding RNAs stacking strategy tree-based algorithms feature extraction sequence analysis bioinformatics

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The use of classification and regression trees in clinical epidemiology

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JOURNAL OF CLINICAL EPIDEMIOLOGY 2001年第6期54卷 603-609页

作者： Marshall, RJ Univ Auckland Dept Community Hlth Auckland New Zealand

A critique is presented of the use of tree-based partitioning algorithms to formulate classification rules and identify subgroups from clinical and epidemiological data. It is argued that the methods have a number of limitations, despite their popularity and apparent closeness to clinical reasoning processes. The issue of redundancy in tree-derived decision rules is discussed. Simple rules may be unlikely to be "discovered" by tree growing. Subgroups identified by trees are often hard to interpret or believe and net effects are not assessed. These problems arise fundamentally because trees are hierarchical. Newer refinements of tree technology seem unlikely to be useful, wedded as they are to hierarchical structures. (C) 2001 Elsevier Science Inc. Ah rights reserved.

关键词： classification rules tree-based algorithms clinical epidemiology

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Adaptive quarklet tree approximation

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ADVANCES IN COMPUTATIONAL MATHEMATICS 2024年第6期50卷 1-33页

作者： Dahlke, Stephan Hovemann, Marc Raasch, Thorsten Vogel, Dorian Philipps Univ Marburg Fachbereich Math & Informat Hans Meerwein Str D-35032 Marburg Germany Univ Siegen Dept Math Walter Flex Str 3 D-57072 Siegen Germany

This paper is concerned with near-optimal approximation of a given univariate function with elements of a polynomially enriched wavelet frame, a so-called quarklet frame. Inspired by hp-approximation techniques of Binev, we use the underlying tree structure of the frame elements to derive an adaptive algorithm that, under standard assumptions concerning the local errors, can be used to create approximations with an error close to the best tree approximation error for a given cardinality. We support our findings by numerical experiments demonstrating that this approach can be used to achieve inverse-exponential convergence rates.

关键词： Adaptive numerical algorithms hp-refinement Near-best approximation Quarkonial decompositions tree-based algorithms Wavelets

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Assessing the shear strength of sandy soil reinforced with polyethylene-terephthalate: an AI-based approach

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EARTH SCIENCE INFORMATICS 2024年第5期17卷 4507-4526页

作者： Samaei, Masoud Alinejad Omran, Morteza Keramati, Mohsen Naderi, Reza Shirani Faradonbeh, Roohollah Curtin Univ WA Sch Mines Minerals Energy & Chem Engn Kalgoorlie WA 6430 Australia Shahrood Univ Technol Dept Civil Engn Shahrood Iran

This research aimed to investigate the effectiveness of Polyethylene-Terephthalate (PET) as a reinforcement material for sandy soils in enhancing the shear strength. To achieve this, different concentrations of PET were tested, and 118 sets of data were collected. Parameters such as relative density, normal stress in direct shear strength test, and types of PET elements (1 x 1, 1 x 5, and fiber) were also recorded. Subsequently, four decision tree-oriented machine learning (ML) methods-decision tree (DT), random forest (RF), AdaBoost, and XGBoost-were applied to construct models capable of forecasting enhancements in shear strength. The evaluation of these models' effectiveness was conducted using four established statistical metrics: R2, RMSE, VAF, and A-10. The results showed that AdaBoost results in the highest prediction accuracy among other algorithms, representing the high modelling performance of the algorithm in dealing with complex nonlinear problems. The conducted sensitivity analysis also revealed that relative density is the most crucial parameter for all the algorithms in predicting the output, followed by PET percentage and normal stress. Furthermore, to make the developed model in this study more practical and easy to use, a Graphical User Interface (GUI) was created, enabling the engineers and researchers to perform the analysis straightforwardly.

关键词： Polyethylene terephthalate Soil improvement AdaBoost XGBoost tree-based algorithms

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Selecting essential factors for predicting reference crop evapotranspiration through tree-based machine learning and Bayesian optimization

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THEORETICAL AND APPLIED CLIMATOLOGY 2024年第4期155卷 2953-2972页

作者： Zhao, Long Wang, Yuhang Shi, Yi Zhao, Xinbo Cui, Ningbo Zhang, Shuo Henan Univ Sci & Technol Coll Agr Equipment Engn Luoyang 471000 Henan Peoples R China Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu 610065 Sichuan Peoples R China Sichuan Univ Coll Water Resource & Hydropower Chengdu 610065 Sichuan Peoples R China

Reference crop evapotranspiration (ETO) is a basic component of the hydrological cycle and its estimation is critical for agricultural water resource management and scheduling. In this study, three tree-based machine learning algorithms (random forest [RF], gradient boosting decision tree [GBDT], and extreme gradient boosting [XGBoost]) were adopted to determine the essential factors for ETO prediction. The tree-based models were optimized using the Bayesian optimization (BO) algorithm and were compared with three standalone models in terms of daily ETO and monthly mean ETO estimations in North China, with different input combinations of essential variables. The results indicated that solar radiation (Rs) and air temperature (Ts), including the maximum, minimum, and average temperatures, in daily ETO were the key variables affecting model prediction accuracy. Rs was the most influential factor in the monthly average ETO model followed by Ts. Both relative humidity (RH) and wind speed at 2 m (U2) had little impact on ETO prediction at different scales, although their importance differed. Compared with the GBDT and RF models, the XGBoost model exhibited the best performance for daily ETO and monthly mean ETO estimations. The hybrid tree-based models with the BO algorithm outperformed standalone tree-based models. Overall, compared with the other inputs, the model with three inputs (Rs, Ts, and RH/U2) had the highest accuracy. The BO-XGBoost model exhibited superior performance in terms of the global performance index (GPI) for daily ETO and monthly mean ETO predictions and is recommended as a more accurate model for predicting daily ETO and monthly mean ETO in North China or areas with a similar climate.

关键词： Reference crop evapotranspiration Essential factors tree-based algorithms Machine learning Bayesian optimization

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Balancing Fined-Tuned Machine Learning Models Between Continuous and Discrete Variables - A Comprehensive Analysis Using Educational Data 23rd

Balancing Fined-Tuned Machine Learning Models Between Contin...

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23rd International Conference on Artificial Intelligence in Education (AIED)

作者： Drousiotis, Efthyvoulos Pentaliotis, Panagiotis Shi, Lei Cristea, Alexandra, I Univ Liverpool Dept Elect Engn & Elect Liverpool Merseyside England Univ Durham Dept Comp Sci Durham England

ISBN: (纸本)9783031116445;9783031116438

Along with the exponential increase of students enrolling in MOOCs [26] arises the problem of a high student dropout rate. Researchers worldwide are interested in predicting whether students will drop out of MOOCs to prevent it. This study explores and improves ways of handling notoriously challenging continuous variables datasets, to predict dropout. Importantly, we propose a fair comparison methodology: unlike prior studies and, for the first time, when comparing various models, we use algorithms with the dataset they are intended for, thus `like for like.' We use a time-series dataset with algorithms suited for time-series, and a converted discrete-variables dataset, through feature engineering, with algorithms known to handle discrete variables well. Moreover, in terms of predictive ability, we examine the importance of finding the optimal hyperparameters for our algorithms, in combination with the most effective pre-processing techniques for the data. We show that these much lighter discrete models outperform the time-series models, enabling faster training and testing. This result also holds over fine-tuning of pre-processing and hyperparameter optimisation.

关键词： Neural networks tree-based algorithms Educational data mining Feature engineering MOOCs

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