Robust Classification Based on Correlations Between Attributes

Νανόπουλος, Αλέξανδρος; Papadopoulos, Apostolos N.; Manolopoulos, Yannis; Welzer-Druzovec, Tatjana

doi:10.4018/jdwm.2007070102

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…These features can then be used as input in classic (non-longitudinal) clustering algorithms, such as Kmeans or hierarchical clustering. We extracted the most common features: mean, standard deviation, kurtosis and skewness [39]. The kurtosis and the skewness describe the shape of the distribution of longitudinal data.…”

Section: Feature-based Approachmentioning

confidence: 99%

Tracking clusters of patients over time enables extracting information from medico-administrative databases

LAMBERT

Leutenegger

Jannot

et al. 2023

Journal of Biomedical Informatics

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Section: Feature-based Approachmentioning

confidence: 99%

Tracking clusters of patients over time enables extracting information from medico-administrative databases

LAMBERT

Leutenegger

Jannot

et al. 2023

Journal of Biomedical Informatics

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“…The most common machine learning techniques used for TSC are SVM (Rodríguez and Alonso, 2004;Xing and Keogh, 2010), KNN (Li et al, 2013;Xing and Keogh, 2010), decision tree (DT;Brunello et al, 2019;Jović et al, 2012), and multilayer perceptron (MLP;del Campo et al, 2021;Nanopoulos et al, 2001). These models and their applications in TSC are beyond the scope of this study and will not be further addressed.…”

Section: Machine Learning Models For Tscmentioning

confidence: 99%

Convolutional neural network and long short-term memory models for ice-jam predictions

Madaeni¹,

Chokmani²,

Lhissou³

et al. 2022

The Cryosphere

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Abstract. In cold regions, ice jams frequently result in severe flooding due to a rapid rise in water levels upstream of the jam. Sudden floods resulting from ice jams threaten human safety and cause damage to properties and infrastructure. Hence, ice-jam prediction tools can give an early warning to increase response time and minimize the possible damages. However, ice-jam prediction has always been a challenge as there is no analytical method available for this purpose. Nonetheless, ice jams form when some hydro-meteorological conditions happen, a few hours to a few days before the event. Ice-jam prediction can be addressed as a binary multivariate time-series classification. Deep learning techniques have been widely used for time-series classification in many fields such as finance, engineering, weather forecasting, and medicine. In this research, we successfully applied convolutional neural networks (CNN), long short-term memory (LSTM), and combined convolutional–long short-term memory (CNN-LSTM) networks to predict the formation of ice jams in 150 rivers in the province of Quebec (Canada). We also employed machine learning methods including support vector machine (SVM), k-nearest neighbors classifier (KNN), decision tree, and multilayer perceptron (MLP) for this purpose. The hydro-meteorological variables (e.g., temperature, precipitation, and snow depth) along with the corresponding jam or no-jam events are used as model inputs. Ten percent of the data were excluded from the model and set aside for testing, and 100 reshuffling and splitting iterations were applied to 80 % of the remaining data for training and 20 % for validation. The developed deep learning models achieved improvements in performance in comparison to the developed machine learning models. The results show that the CNN-LSTM model yields the best results in the validation and testing with F1 scores of 0.82 and 0.92, respectively. This demonstrates that CNN and LSTM models are complementary, and a combination of both further improves classification.

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“…Other classification techniques are also discussed in the literature, including techniques based on fuzzy logic (Khabbaz et al, 2008), multi-label classification (Tsoumakas et al, 2007), and robust classification based on correlations between attributes (Nanopoulos, 2007).…”

Section: Related Workmentioning

confidence: 99%

Classification of Peer-to-Peer Traffic Using A Two-Stage Window-Based Classifier With Fast Decision Tree and IP Layer Attributes

Raahemi

Mumtaz

2010

International Journal of Data Warehousing and Mining

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This paper presents a new approach using data mining techniques, and in particular a two-stage architecture, for classification of Peer-to-Peer (P2P) traffic in IP networks where in the first stage the traffic is filtered using standard port numbers and layer 4 port matching to label well-known P2P and NonP2P traffic. The labeled traffic produced in the first stage is used to train a Fast Decision Tree (FDT) classifier with high accuracy. The Unknown traffic is then applied to the FDT model which classifies the traffic into P2P and NonP2P with high accuracy. The two-stage architecture not only classifies well-known P2P applications, but also classifies applications that use random or non-standard port numbers and cannot be classified otherwise. The authors captured the internet traffic at a gateway router, performed pre-processing on the data, selected the most significant attributes, and prepared a training data set to which the new algorithm was applied. Finally, the authors built several models using a combination of various attribute sets for different ratios of P2P to NonP2P traffic in the training data.

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Robust Classification Based on Correlations Between Attributes

Cited by 7 publications

References 12 publications

Tracking clusters of patients over time enables extracting information from medico-administrative databases

Tracking clusters of patients over time enables extracting information from medico-administrative databases

Convolutional neural network and long short-term memory models for ice-jam predictions

Classification of Peer-to-Peer Traffic Using A Two-Stage Window-Based Classifier With Fast Decision Tree and IP Layer Attributes

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