A semi-hard voting combiner scheme to ensemble multi-class probabilistic classifiers

Delgado, Rosario

doi:10.1007/s10489-021-02447-7

Cited by 12 publications

(6 citation statements)

References 23 publications

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“…Due to data dependency, distinct learners learn in succession using the sequential ensemble technique. [20] As a result, as seen in Fig. 4, the second model successively corrects the faults produced by the first model.…”

Section: Training Baseline Classifiersmentioning

confidence: 76%

A Review on Ensemble Machine and Deep Learning Techniques Used in the Classification of Computed Tomography Medical Images

Imran,

2024

Int J Health Sci Res

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Ensemble learning combines multiple base models to enhance predictive performance and generalize better on unseen data. In the context of Computed Tomography (CT) image processing, ensemble techniques often leverage diverse machine learning or deep learning architectures to achieve the best results. Ensemble machine learning and deep learning techniques have revolutionized the field of CT image processing by significantly improving accuracy, robustness, and efficiency in various medical imaging tasks. These methods have been instrumental in tasks such as image reconstruction, segmentation, classification, and disease diagnosis. The ensemble models can be divided into those based on decision fusion strategies, bagging, boosting, stacking, negative correlation, explicit/implicit ensembles, homogeneous/heterogeneous ensembles, and explicit/implicit ensembles. In comparison to shallow or traditional, machine learning models and deep learning architectures are currently performing better. Also, a brief discussion of the various ensemble models used in CT images is provided. We wrap up this work by outlining a few possible avenues for further investigation. Key words: Computed Tomography, Ensemble, Deep learning, Machine Learning.

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Section: Training Baseline Classifiersmentioning

confidence: 76%

A Review on Ensemble Machine and Deep Learning Techniques Used in the Classification of Computed Tomography Medical Images

Imran,

2024

Int J Health Sci Res

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“…As such, we created our proposed model by assigning different weights to these classifiers and feeding their predictions to an integrated model, which is also known as ensemble learning. The voting algorithm is one of the simplest, most popular, and effective combiner schemes for ensemble learning [ 45 , 46 ]. It fuses the results from various learning algorithms to achieve knowledge discovery and better predictive performance [ 47 , 48 , 49 ].…”

Section: Methodsmentioning

confidence: 99%

Pilot Behavior Recognition Based on Multi-Modality Fusion Technology Using Physiological Characteristics

Wang

et al. 2022

Biosensors

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With the development of the autopilot system, the main task of a pilot has changed from controlling the aircraft to supervising the autopilot system and making critical decisions. Therefore, the human–machine interaction system needs to be improved accordingly. A key step to improving the human–machine interaction system is to improve its understanding of the pilots’ status, including fatigue, stress, workload, etc. Monitoring pilots’ status can effectively prevent human error and achieve optimal human–machine collaboration. As such, there is a need to recognize pilots’ status and predict the behaviors responsible for changes of state. For this purpose, in this study, 14 Air Force cadets fly in an F-35 Lightning II Joint Strike Fighter simulator through a series of maneuvers involving takeoff, level flight, turn and hover, roll, somersault, and stall. Electro cardio (ECG), myoelectricity (EMG), galvanic skin response (GSR), respiration (RESP), and skin temperature (SKT) measurements are derived through wearable physiological data collection devices. Physiological indicators influenced by the pilot’s behavioral status are objectively analyzed. Multi-modality fusion technology (MTF) is adopted to fuse these data in the feature layer. Additionally, four classifiers are integrated to identify pilots’ behaviors in the strategy layer. The results indicate that MTF can help to recognize pilot behavior in a more comprehensive and precise way.

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“…Delgado [118] proposed, based on the confidence level CL that assigns a degree of support of each weak learner and bagging approach, a voting scheme. The degree of support measure depends on the probabilistic of the error of each individual classifier.…”

Section: Related Workmentioning

confidence: 99%

Ensemble learning based on relative accuracy approach and diversity teams

B. Rokaya,

D. Alsufiani

2024

Bulletin EEI

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Ensemble learning, which involves combining the opinions of multiple experts to arrive at a better result, has been used for centuries. In this work, a review of the major voting methods in ensemble learning is explored. This work will focus on a new method for combining the results of individual learners. The method depends on the relative accuracy and diversity of teams. Instead of trying to assign weight to each different trainer, the concept of diversity teams is presented. Each team will vote as one player; however, the individual accuracies of each learner still be implemented. The concept of relaxing parameters that deal with each team as one player is presented. Our experiments demonstrate that traditional ensemble voting methods outperform individual learners. There is a limit to the superiority of the ensemble learner that any ensemble learner cannot go beyond. The proposed voting method gives the same results as the traditional ensemble voting methods, however, a different diversity of the proposed method from the traditional voting method or for different values of the relaxing parameter can be achieved.

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A semi-hard voting combiner scheme to ensemble multi-class probabilistic classifiers

Cited by 12 publications

References 23 publications

A Review on Ensemble Machine and Deep Learning Techniques Used in the Classification of Computed Tomography Medical Images

A Review on Ensemble Machine and Deep Learning Techniques Used in the Classification of Computed Tomography Medical Images

Pilot Behavior Recognition Based on Multi-Modality Fusion Technology Using Physiological Characteristics

Ensemble learning based on relative accuracy approach and diversity teams

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