Important Complexity Reduction of Random Forest in Multi-Classification Problem

Hassine, Kawther; Erbad, Aiman; Hamila, Ridha

doi:10.1109/iwcmc.2019.8766544

Cited by 33 publications

(23 citation statements)

References 14 publications

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“…73 The RF is characterized by a low number of control where the complexity of the prediction phase is O(set × tree) where set is the feature set size and tree is the number of trees. 74…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Mobile‐aided screening system for proliferative diabetic retinopathy

Boukadida

Elloumi

Akil

et al. 2021

Int J Imaging Syst Tech

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NeoVascularization (NV) occurs in the Proliferative Diabetic Retinopathy (PDR) stage, where the development progress of new vessels presents a high risk for severe vision loss and blindness. Therefore, early NV detection is primordial to preserve patient's vision. Several automated methods have been proposed to detect the NV on retinograph-captured fundus images. However, their employment is constrained by the reduced ophthalmologist-per-person ratio and the expensive equipment for image capturing. This paper presents a novel method for NV detection in smartphone-captured fundus images. The implementation of the method on a smartphone having an optical lens for fundus capturing leads to a Mobiles-Aided-Screening system of PDR (MAS-PDR). The challenge is to ensure accurate and robust detection even with moderate quality of fundus image, on reduced execution time.Within this objective, we identify the major criteria of neovascularized vessels which are tortuosity, width, bifurcation, and density. Our main contribution consists in proposing a sharp feature to reflect each criterion on reduced computational complexity processing. Therefore, the features are provided to a random forest classifier to deduce the PDR stage.A dataset raised from publicly databases is used on a 10-cross validation process where 98.69% accuracy, 97.73% sensitivity, and 99.12% specificity are achieved. To evaluate the robustness, the same experimentation is repeated after applying motion blur filters to the fundus image dataset, where 98.91% accuracy, 96.75% sensitivity, and 100% specificity are deduced. Moreover, NV screening is performed under 3 s when executed in smartphone devices demonstrating the appropriateness of our method to MAS-PDR.

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“…73 The RF is characterized by a low number of control where the complexity of the prediction phase is O(set × tree) where set is the feature set size and tree is the number of trees. 74…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Mobile‐aided screening system for proliferative diabetic retinopathy

Boukadida

Elloumi

Akil

et al. 2021

Int J Imaging Syst Tech

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“…For the task of Intrusion Detection [2,3,18,70,71,80], a variety of different approaches have been used in the literature including Ensemble methods [53], Feature Selection [52], Fuzzy Neural Networks [13], Kernel Methods [67], Random Forests [21], and deep [25]. However, we refrain from comparing with these approaches as they do not process the data in a streaming manner and typically require large amount of labelled training data, whereas we process the data in an unsupervised and online manner.…”

Section: Related Workmentioning

confidence: 99%

MStream: Fast Anomaly Detection in Multi-Aspect Streams

Bhatia

Jain

et al. 2021

Proceedings of the Web Conference 2021

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Given a stream of entries in a multi-aspect data setting i.e., entries having multiple dimensions, how can we detect anomalous activities in an unsupervised manner? For example, in the intrusion detection setting, existing work seeks to detect anomalous events or edges in dynamic graph streams, but this does not allow us to take into account additional attributes of each entry. Our work aims to define a streaming multi-aspect data anomaly detection framework, termed MStream which can detect unusual group anomalies as they occur, in a dynamic manner. MStream has the following properties: (a) it detects anomalies in multi-aspect data including both categorical and numeric attributes; (b) it is online, thus processing each record in constant time and constant memory; (c) it can capture the correlation between multiple aspects of the data. MStream is evaluated over the KDDCUP99, CICIDS-DoS, UNSW-NB 15 and CICIDS-DDoS datasets, and outperforms state-of-the-art baselines.

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“…Complexity may refer to the time required to build the model, the computer memory consumed, or the amount of time a program runs until a result is obtained. The complexity of training the random forest classifier is O (M•m•n•log(n)), where M is the number of decision trees in the random forest, m is the number of variables, and n is the number of samples in the training set [16]. This means that reducing the number of input parameters will shorten training time by half.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

“…Figure 1 illustrates the block diagram of the recursive algorithm allowing the development of the models. number of variables, and n is the number of samples in the training set [16]. This mean that reducing the number of input parameters will shorten training time by half.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Artificial Intelligence—A Tool for Risk Assessment of Delayed-Graft Function in Kidney Transplant

Konieczny

Stojanowski

Rydzyńska

et al. 2021

JCM

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Delayed-graft function (DGF) might be responsible for shorter graft survival. Therefore, a clinical tool predicting its occurrence is vital for the risk assessment of transplant outcomes. In a single-center study, we conducted data mining and machine learning experiments, resulting in DGF predictive models based on random forest classifiers (RF) and an artificial neural network called multi-layer perceptron (MLP). All designed models had four common input parameters, determining the best accuracy and discriminant ability: donor’s eGFR, recipient’s BMI, donor’s BMI, and recipient–donor weight difference. RF and MLP designs, using these parameters, achieved an accuracy of 84.38% and an area under curve (AUC) 0.84. The model additionally implementing a donor’s age, gender, and Kidney Donor Profile Index (KDPI) accomplished an accuracy of 93.75% and an AUC of 0.91. The other configuration with the estimated post-transplant survival (EPTS) and the kidney donor risk profile (KDRI) achieved an accuracy of 93.75% and an AUC of 0.92. Using machine learning, we were able to assess the risk of DGF in recipients after kidney transplant from a deceased donor. Our solution is scalable and can be improved during subsequent transplants. Based on the new data, the models can achieve better outcomes.

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Important Complexity Reduction of Random Forest in Multi-Classification Problem

Cited by 33 publications

References 14 publications

Mobile‐aided screening system for proliferative diabetic retinopathy

Mobile‐aided screening system for proliferative diabetic retinopathy

MStream: Fast Anomaly Detection in Multi-Aspect Streams

Artificial Intelligence—A Tool for Risk Assessment of Delayed-Graft Function in Kidney Transplant

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