Nonparametric bagging clustering methods to identify latent structures from a sequence of dependent categorical data

Abramowicz, Konrad; Luna, Sara Sjöstedt de; Strandberg, Johan

doi:10.1016/j.csda.2022.107583

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Furthermore, the advantages of the RF are that they are less sensitive to the outliers in the dataset and do not require much parameter tuning. Bagging (BG) is also an ensemble meta-algorithm, which can improve the stability and accuracy of machine learning algorithms [33]. In our study, we integrate bagging into DT methods to reduce the variance of DTs.…”

Section: Tree-based Modelsmentioning

confidence: 99%

Surfacing Positioning Point Prediction of Underwater Glider with a New Combination Model

Zhang

Niu

Wan

et al. 2023

JMSE

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Combination prediction models have gained great development in the area of information science, and are widely applied in engineering fields. The underwater glider (UG) is a new type of unmanned vehicle used in ocean observation for the advantages of long endurance, low noise, etc. However, due to its lower speed relative to the ocean current, the surfacing positioning point (SPP) of an UG often drifts greatly away from the preset waypoint. Therefore, this paper proposes a new combination model for predicting the SPP at different time scales. First, the kinematic model and working flow of the Petrel-L glider is analyzed. Then, this paper introduces the principles of a newly proposed combination model which integrates single prediction models with optimal weight. Afterwards, to make an accurate prediction, ocean current data are interpolated and averaged according to the diving depth of UGs as an external influencing factor. Meanwhile, with sea trial data collected in the northern South China Sea by Petrel-L, which had a total range of 4230.5 km, SPPs are predicted using single prediction models at different time scales, and the combination weights are derived with a novel simulated annealing optimized Frank–Wolfe method. Finally, the evaluated results demonstrate that the MAE and MSE are 966 m and 969 m, which proves that the single models achieved good performance under specified situations, and the combination model performed better at full scale because it integrates the advantages of the single models. Furthermore, the predicted SPPs will be helpful in the dead reckoning of the UG, and the proposed new combination method could extend into other fields for prediction.

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Section: Tree-based Modelsmentioning

confidence: 99%

Surfacing Positioning Point Prediction of Underwater Glider with a New Combination Model

Zhang

Niu

Wan

et al. 2023

JMSE

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A Novel Hierarchical High-Dimensional Unsupervised Active Learning Method

Haghzad Klidbary,

Javadian

2024

Int J Comput Intell Syst

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This paper processes a novel hierarchical high-dimensional clustering algorithm based on the Active Learning Method (ALM), which is a fuzzy-learning algorithm. The hierarchical part of the algorithm is composed of two phases: divisible and agglomerative. The divisible phase, a zooming-in-process, searches for sub-clusters in already-found clusters hierarchically. At each level of the hierarchy, the clusters are found by an ensemble clustering method based on the density of data. This part of the algorithm blurs each data point as multiple one-dimensional fuzzy membership functions called ink-drop patterns; then, it accumulates the ink-drop patterns of all data points on every dimension separately. Next, it performs one-dimensional density partitioning to produce an ensemble of clustering solutions; after that, combining the results is done based on a novel consensus method with the aid of prime numbers. An agglomerative phase is a bottom-up approach that merges clusters based on a novel distance metric, named $${K}^{2}$$ K 2 -nearest neighbor. The algorithm is named as the Hierarchical High-Dimensional Unsupervised Active Learning Method (HiDUALM) and is explained in more detail throughout this paper. Although the classical clustering methods are not suitable for high-dimensional data clustering, the proposed method solves the problems related to speed and memory using ensemble learning, while due to its hierarchy and the use of different distance criteria, different levels of the cluster provide the clause. Experiments on synthetic and real-world datasets are presented to show the effectiveness of the proposed-clustering algorithm.

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An algorithmic approach to identification of gray areas: Analysis of sleep scoring expert ensemble non agreement areas using a multinomial mixture model

Jouan,

Arnardottir,

Islind

et al. 2024

European Journal of Operational Research

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Nonparametric bagging clustering methods to identify latent structures from a sequence of dependent categorical data

Cited by 3 publications

References 16 publications

Surfacing Positioning Point Prediction of Underwater Glider with a New Combination Model

Surfacing Positioning Point Prediction of Underwater Glider with a New Combination Model

A Novel Hierarchical High-Dimensional Unsupervised Active Learning Method

An algorithmic approach to identification of gray areas: Analysis of sleep scoring expert ensemble non agreement areas using a multinomial mixture model

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