PRF-RW: a progressive random forest-based random walk approach for interactive semi-automated pulmonary lobes segmentation

Li, Qiang; Chen, Lei; Li, Xiangju; Lv, Xiaoyi; Xia, Shunren; Kang, Yan

doi:10.1007/s13042-020-01111-9

Cited by 8 publications

(2 citation statements)

References 48 publications

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“…Regarding regression problems, the mean of the individual trees is considered as an output. RF neutralizes the tendency of decision trees to overfit on the training data [ 42 , 43 ]. The three common methodologies used are: forest RI (random forest choice), forest RC (random blend), and a combination of forest RI and forest RC.…”

Section: Scientific Backgroundmentioning

confidence: 99%

A Hybrid Generic Framework for Heart Problem Diagnosis Based on a Machine Learning Paradigm

Menshawi

Hassan

Allheeib

et al. 2023

Sensors

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The early, valid prediction of heart problems would minimize life threats and save lives, while lack of prediction and false diagnosis can be fatal. Addressing a single dataset alone to build a machine learning model for the identification of heart problems is not practical because each country and hospital has its own data schema, structure, and quality. On this basis, a generic framework has been built for heart problem diagnosis. This framework is a hybrid framework that employs multiple machine learning and deep learning techniques and votes for the best outcome based on a novel voting technique with the intention to remove bias from the model. The framework contains two consequent layers. The first layer contains simultaneous machine learning models running over a given dataset. The second layer consolidates the outputs of the first layer and classifies them as a second classification layer based on novel voting techniques. Prior to the classification process, the framework selects the top features using a proposed feature selection framework. It starts by filtering the columns using multiple feature selection methods and considers the top common features selected. Results from the proposed framework, with 95.6% accuracy, show its superiority over the single machine learning model, classical stacking technique, and traditional voting technique. The main contribution of this work is to demonstrate how the prediction probabilities of multiple models can be exploited for the purpose of creating another layer for final output; this step neutralizes any model bias. Another experimental contribution is proving the complete pipeline’s ability to be retrained and used for other datasets collected using different measurements and with different distributions.

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Section: Scientific Backgroundmentioning

confidence: 99%

A Hybrid Generic Framework for Heart Problem Diagnosis Based on a Machine Learning Paradigm

Menshawi

Hassan

Allheeib

et al. 2023

Sensors

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show abstract

“…At each instant n, the action probability vector pi(n) is updated by the linear learning algorithm given in equation ( 13) if the chosen action ai(k) is rewarded by the environment, and it is updated according to equation ( 14) if the chosen action is penalized [104]. [11], [12], [13] Global problem [26], [27], [28] Healthcare [32], [33], [34], [35], [36], [41], [98], [37], [39], [40], [42], [43], [45], [46], [47], [48], [49], [50], [51], Industrial [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62] Network [63], [67], [68], [69], [99], [100] Physics [71], [72] Text processing …”

Section: Learning Automatamentioning

confidence: 99%

A New Random Forest Algorithm Based on Learning Automata

Savargiv

Masoumi

Keyvanpour

2021

Computational Intelligence and Neuroscience

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The goal of aggregating the base classifiers is to achieve an aggregated classifier that has a higher resolution than individual classifiers. Random forest is one of the types of ensemble learning methods that have been considered more than other ensemble learning methods due to its simple structure, ease of understanding, as well as higher efficiency than similar methods. The ability and efficiency of classical methods are always influenced by the data. The capabilities of independence from the data domain, and the ability to adapt to problem space conditions, are the most challenging issues about the different types of classifiers. In this paper, a method based on learning automata is presented, through which the adaptive capabilities of the problem space, as well as the independence of the data domain, are added to the random forest to increase its efficiency. Using the idea of reinforcement learning in the random forest has made it possible to address issues with data that have a dynamic behaviour. Dynamic behaviour refers to the variability in the behaviour of a data sample in different domains. Therefore, to evaluate the proposed method, and to create an environment with dynamic behaviour, different domains of data have been considered. In the proposed method, the idea is added to the random forest using learning automata. The reason for this choice is the simple structure of the learning automata and the compatibility of the learning automata with the problem space. The evaluation results confirm the improvement of random forest efficiency.

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Enhanced lung cancer detection: Integrating improved random walker segmentation with artificial neural network and random forest classifier

Nair,

Devi,

Bhasi

2024

Heliyon

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PRF-RW: a progressive random forest-based random walk approach for interactive semi-automated pulmonary lobes segmentation

Cited by 8 publications

References 48 publications

A Hybrid Generic Framework for Heart Problem Diagnosis Based on a Machine Learning Paradigm

A Hybrid Generic Framework for Heart Problem Diagnosis Based on a Machine Learning Paradigm

A New Random Forest Algorithm Based on Learning Automata

Enhanced lung cancer detection: Integrating improved random walker segmentation with artificial neural network and random forest classifier

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