Adaboost face detector based on Joint Integral Histogram and Genetic Algorithms for feature extraction process

Jammoussi, Ameni Yangui; Ghribi, Sameh Fakhfakh; Masmoudi, Dorra Sellami

doi:10.1186/2193-1801-3-355

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…The Adaboost algorithm is one of the 10 most famous data mining algorithms, and it has been widely applied in areas such as face recognition [ 26 ]. The experimental results of this study show that this algorithm and LogitBoost, which is an improved version of Adaboost, show excellent performance in the modelling of disease classification by using clinical medical data.…”

Section: Discussionmentioning

confidence: 99%

Diabetes classification model based on boosting algorithms

Chen

Pan

2018

BMC Bioinformatics

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BackgroundDiabetes mellitus is a common and complicated chronic lifelong disease. Hence, it is of high clinical significance to find the most relevant clinical indexes and to perform efficient computer-aided pre-diagnoses and diagnoses.ResultsNon-parametric statistical testing is performed on hundreds of medical measurement index results between diabetic and non-diabetic populations. Two common boosting algorithms, Adaboost.M1 and LogitBoost, are selected to establish a machine model for diabetes diagnosis based on these clinical test data, involving a total of 35,669 individuals. The machine classification models built by these two algorithms have very good classification ability. Here, the LogitBoost classification model is slightly better than the Adaboost.M1 classification model. The overall accuracy of the LogitBoost classification model reached 95.30% when using 10-fold cross validation. The true positive, true negative, false positive, and false negative rates of the binary classification model were 0.921, 0.969, 0.031, and 0.079, respectively, and the area under the receiver operating characteristic curve reached 0.99.ConclusionsThe boosting algorithms show excellent performance for the diabetes classification models based on clinical medical data. The coefficient matrix of the original data is a sparse matrix, because some of the test results were missing, including some that were directly related to disease diagnosis. Therefore, the model is robust and has a degree of pre-diagnosis function. In the process of selecting the preferred test items, the most statistically significant discriminating factors between the diabetic and general populations were obtained and can be used as reference risk factors for diabetes mellitus.

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Section: Discussionmentioning

confidence: 99%

Diabetes classification model based on boosting algorithms

Chen

Pan

2018

BMC Bioinformatics

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“…At present, the algorithm is widely used in face detection, machine vision, speech recognition, weather prediction, fault diagnosis and other fields. 28–34…”

Section: Introductionmentioning

confidence: 99%

“…At present, the algorithm is widely used in face detection, machine vision, speech recognition, weather prediction, fault diagnosis and other fields. [28][29][30][31][32][33][34] Based on the above references, the robust control strategy based on AdaBoost prediction is put forward, and the friction model of system and linear uncertain generalized equation of state are established. 23,35,36 According to the robust control theory, the H ' robust controller is designed, and a regression model based on RBF neural network is designed.…”

Section: Introductionmentioning

confidence: 99%

Research on robust control of continuous rotary electro-hydraulic servo motor based on AdaBoost prediction

Wang

Shi

Liu

et al. 2022

Measurement and Control

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A robust control strategy based on Adaboost prediction was proposed for the effects of dynamic uncertainty, parameter perturbation and nonlinear factors such as friction and leakage on the robustness and low-speed performance of the electro-hydraulic servo system. Based on the establishment of the Pol-Ind friction model, according to the mathematical model of the continuous rotary electro-hydraulic servo motor, the structural uncertainty model of the system under parameter perturbation and external perturbation and the generalized state equation of the system are established. The robust controller is designed according to the [Formula: see text] control theory. The system feedback mechanism is designed by using the AdaBoost based on the RBF neural network algorithm. The real-time input and output of the system are used as the training sample set for repeated training, and the multiple weak neural network predictors obtained by training are used to form a strong predictor to calculate the forecast output and error of the electro-hydraulic servo system, and the robust controller was used for real-time control. Simulation and experiment show that: compared with the wavelet neural network control, the robust controller based on AdaBoost prediction has a good tracking performance to the slope signal with an input signal of 0.001°/s, and has a high response speed to sinusoidal signal. Under the condition of satisfying the requirement of double 10 index, the maximum response frequency reaches 15 Hz. It can effectively improve the low-speed performance and anti-interference ability of the electro-hydraulic servo system, greatly improve the robustness of the system and expand the frequency band of the system.

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“…It is used to train multi‐weak learners, and assign weights to each weak learner, then boost multi‐weak learners into a strong one with abilities of high generalisation and non‐linear reflecting, which can eliminate the over‐fitting phenomenon effectively [11, 12]. Nowadays, the algorithm is widely employed for face recognition, short‐term wind speed prediction, furnace temperature, fault diagnosis, and others [13–17].…”

Section: Introductionmentioning

confidence: 99%