Feature Learning Viewpoint of Adaboost and a New Algorithm

Wang, Fei; Li, Zhongheng; He, Fang; Wang, Rong; Yu, Weizhong; Nie, Feiping

doi:10.1109/access.2019.2947359

Cited by 56 publications

(42 citation statements)

References 27 publications

(34 reference statements)

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“…In this paper, we will choose the two most important features according to the importance of the features to build a two-dimensional feature space. By visualizing decision boundaries, we can intuitively see the distribution of different patterns in the feature space and the classification effect of different classifiers such as SVM [29], GBDT [30], RF [31] and Adaboost [32].…”

Section: The Classifier Performancementioning

confidence: 99%

Multi-Class Disturbance Events Recognition Based on EMD and XGBoost in φ-OTDR

et al. 2020

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A novel pattern recognition method based on Empirical Mode Decomposition (EMD) and extreme gradient boosting (XGBoost) is proposed to recognize the disturbance events in phase sensitive optical time-domain reflectometer (ϕ-OTDR) to reduce nuisance alarm rate (NAR) and improve real-time performance in this paper. Eleven typical eigenvectors are extracted from components obtained by EMD of the disturbance signals and XGBoost is selected as a classifier to identify different type of disturbance signals. Five kinds of disturbance events, including watering, knocking, climbing, pressing and false disturbance event, can be identified, effectively. Experimental results show that NAR is 4.10% and identification time is 0.093 s. The recognition accuracy for the five patterns is 97.96%, 95.90%, 91.10%, 94.84% and 99.69%, respectively. The effectiveness of the proposed method is evaluated by using confusion matrix and decision boundary visualization. Experimental results demonstrate that our proposed pattern recognition method based on XGBoost has better performance in recognition rate and recognition time than other commonly used methods, such as support vector machine (SVM), Gradient Boosting Decision Tree (GBDT), Random Forest (RF) and Adaptive Boosting (Adaboost). INDEX TERMS Phase-sensitive optical time-domain reflectometer (ϕ-OTDR), extreme gradient boosting (XGBoost), nuisance alarm rate (NAR), empirical mode decomposition (EMD), pattern recognition, decision boundary visualization.

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Section: The Classifier Performancementioning

confidence: 99%

Multi-Class Disturbance Events Recognition Based on EMD and XGBoost in φ-OTDR

et al. 2020

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“…Due to the relatively small size of the considered Arabic/English speech emotion database (320 utterances), several considerations were made to assure generalization of the created classification models. AdaBoost [65] was implemented alongside the linear SVM classifier in order to avoid overfitting [66]. Moreover, leave-one-out cross validation was used where the number of folds is the same as the number of database instances such that each instance in the database is classified only once using all the other instances for training.…”

Section: Speech Emotion Recognition 1) Methodsmentioning

confidence: 99%

Analysis of Linguistic and Prosodic Features of Bilingual Arabic–English Speakers for Speech Emotion Recognition

2020

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Speech emotion recognition (SER) research has usually focused on the analysis of the native language of speakers, most commonly, targeting European and Asian languages. In the present study, a bilingual Arabic/English speech emotion database elicited from 16 male and 16 female Egyptian participants was created in order to investigate how the linguistic and prosodic features were affected by the anger, fear, happiness and sadness emotions across Arabic and English emotional speech. The results of the linguistic analysis indicated that the participants preferred to express their emotions indirectly, mainly using religious references, and that the female participants tended to use language that was more tentative and emotionally expressive, while the male participants tended to use language that was more assertive and independent. As for the prosodic analysis, statistical t-tests showed that the prosodic features of pitch, intensity and speech rate were more indicative of anger and happiness while less relevant to fear and scarcely significant for sadness. Furthermore, speech emotion recognition performed using linear support vector machine (SVM) with AdaBoost also supported these results. In regard to first and second language linguistic features, there was no significant difference in the choice of words and structures expressing the different emotions in the two languages, but in terms of prosodic features, the females' speech showed higher pitch in Arabic in all cases while both genders showed close intensity values in the two languages and faster speech rate in Arabic than in English. INDEX TERMS Arabic speech analysis, bilingual linguistic and prosodic features, classification, speech emotion recognition. LAMIAA ABDEL-HAMID received the Ph.D. degree (Hons.

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“…In essence, ensemble learning methods are meta-algorithms incorporating many methods of machine learning into one predictive model to improve performance. We selected three ensemble methods based on literature performance on assisting with pandemic predictions [10,11,12]. These are AdaBoost, Bagging and Extra-Trees classifiers.…”

Section: Ensemble Methodsmentioning

confidence: 99%

Maintaining proper health records improves machine learning predictions for novel 2019-nCoV

Khan¹,

Ramsahai²

2020

Preprint

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Abstract Background: An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia continues to affect the whole world including major cities such as China, USA, Italy, France and the United Kingdom. We present outcome ('recovered', 'isolated' or 'death') risk estimates of the 2019-nCoV over 'early' datasets. A major consideration is how likely are people to die from 2019-nCoV? Method: Accounting for the impact of the variations in the reporting rate of 2019-nCoV, we modelled machine learning techniques (AdaBoost, Bagging, Extra-Trees, Decision-Trees and k-Nearest Neighbours Classifiers) on two 2019-nCoV datasets obtained from Kaggle in March 30th 2020. We used 'country', 'age' and 'gender' as features to predict outcome for both datasets. Including the patient's 'disease' history (only present in the second dataset) to predict outcome for the second dataset. Results: The use of a patient's 'disease' history improves the prediction of 'death' by more than a 7-fold. Models ignoring a patent's 'disease' history performed poorly in test predictions. Conclusion: Our findings indicate the potential of using a patient's 'disease' history as part of the feature set in machine learning techniques to improve 2019-nCoV predictions. This can have a positive effect on predictive patient treatment and result in ease for current overburdened healthcare systems worldwide, especially with an increasing prevalence of second and third wave re-infections in some countries.

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Feature Learning Viewpoint of Adaboost and a New Algorithm

Cited by 56 publications

References 27 publications

Multi-Class Disturbance Events Recognition Based on EMD and XGBoost in φ-OTDR

Multi-Class Disturbance Events Recognition Based on EMD and XGBoost in φ-OTDR

Analysis of Linguistic and Prosodic Features of Bilingual Arabic–English Speakers for Speech Emotion Recognition

Maintaining proper health records improves machine learning predictions for novel 2019-nCoV

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