Classification of Skin Sensitizers on the Basis of Their Effective Concentration 3 Values by Using Adaptive Boosting Method

Abstract In this work, a new approach to weather model output postprocessing is presented. The adaptive boosting algorithm is used to train a set of simple base classifiers with historical data from weather model output, surface synoptic observation (SYNOP) messages, and lightning data. The resulting overall method then can be used to classify weather model output to identify potential thunderstorms. The method generates a certainty measure between −1 and 1, describing how likely a thunderstorm is to occur. Using a threshold, the measure can be converted to a binary decision. When compared to a linear discriminant and a method currently employed in an expert system from the German Weather Service, boosting achieves the best validation scores. A substantial improvement of the probability of detection of up to 72% and a decrease of the false alarm rate down to 34% can be achieved for the identification of thunderstorms in model analysis. Independent of the verification results, the method has several useful properties: good cross-validation results, short learning time (≤10 min sequential run time for the experiments on a standard PC), comprehensible inner values of the underlying statistical analysis, and the simplicity of adding predictors to a running system. This paper concludes with a set of possible other applications and extensions to the presented example of thunderstorm detection.

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IDENTIFICATION OF AREAS OF CORONAVIRUS COVID-19 INCIDENCE SPREADING BASED ON CLUSTER ANALYSIS METHOD

Innovative technologies and scientific solutions for industries ◽

10.30837/itssi.2021.15.005 ◽

2021 ◽

pp. 5-13

Author(s):

Kseniia Bazilevych ◽

Ievgen Meniailov ◽

Dmytro Chumachenko

Keyword(s):

Neural Network ◽

Neural Networks ◽

Cluster Analysis ◽

Data Analysis ◽

Gradient Descent ◽

Descent Method ◽

Gradient Descent Method ◽

Adaptive Boosting ◽

Software Product ◽

Boosting Method

Subject: the use of the mathematical apparatus of neural networks for the scientific substantiation of anti-epidemic measures in order to reduce the incidence of diseases when making effective management decisions. Purpose: to apply cluster analysis, based on a neural network, to solve the problem of identifying areas of incidence. Tasks: to analyze methods of data analysis to solve the clustering problem; to develop a neural network method for clustering the territory of Ukraine according to the nature of the epidemic process COVID-19; on the basis of the developed method, to implement a data analysis software product to identify the areas of incidence of the disease using the example of the coronavirus COVID-19. Methods: models and methods of data analysis, models and methods of systems theory (based on the information approach), machine learning methods, in particular the Adaptive Boosting method (based on the gradient descent method), methods for training neural networks. Results: we used the data of the Center for Public Health of the Ministry of Health of Ukraine distributed over the regions of Ukraine on the incidence of COVID-19, the number of laboratory examined persons, the number of laboratory tests performed by PCR and ELISA methods, the number of laboratory tests of IgA, IgM, IgG; the model used data from March 2020 to December 2020, the modeling did not take into account data from the temporarily occupied territories of Ukraine; for cluster analysis, a neural network of 60 input neurons, 100 hidden neurons with an activation Fermi function and 4 output neurons was built; for the software implementation of the model, the programming language Python was used. Conclusions: analysis of methods for constructing neural networks; analysis of training methods for neural networks, including the use of the gradient descent method for the Adaptive Boosting method; all theoretical information described in this work was used to implement a software product for processing test data for COVID-19 in Ukraine; the division of the regions of Ukraine into zones of infection with the COVID-19 virus was carried out and a map of this division was presented.

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Detection and Classification of Leukemia using MPFCM Segmentation and Random Forest with Boosting Techniques

JOURNAL OF ADVANCES IN CHEMISTRY ◽

10.24297/jac.v13i1.4656 ◽

2018 ◽

Vol 13 (1) ◽

pp. 5933-5939

Author(s):

Kalaiselvi Chinnathambi

Keyword(s):

Blood Cell ◽

Classification Accuracy ◽

Visual Inspection ◽

Automatic Segmentation ◽

Segmentation Method ◽

Blood Disorders ◽

Adaptive Boosting ◽

Blast Cells ◽

Fuzzy C Means Algorithm

Identification of blood disorders is through visual inspection of microscopic blood cell images. From the identification of blood disorders lead to classification of certain diseases related to blood. We propose an automatic segmentation method for segmenting White blood cell images. Firstly, modified possibilistic fuzzy c-means algorithm is proposed to detect the contours in the image. The GLCM features are extracted and features are selected by MRMR. Adaptive boosting and LS Boosting has been utilized to classify blast cells from normal lymphocyte cells. Comparison performance of classification accuracy was carried out. The effectiveness of the classification system is tested with the total of 80 samples collected. The evaluated results demonstrate that our method outperformed the existing systems with an accuracy of 88 %.

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Classification of peptide mass fingerprint data by novel no-regret boosting method

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2009.03.006 ◽

2009 ◽

Vol 39 (5) ◽

pp. 460-473 ◽

Cited By ~ 5

Author(s):

Anna Gambin ◽

Ewa Szczurek ◽

Janusz Dutkowski ◽

Magda Bakun ◽

Michał Dadlez

Keyword(s):

Peptide Mass Fingerprint ◽

Peptide Mass ◽

Boosting Method ◽

Fingerprint Data

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Ensemble of ERDTs for Spectral–Spatial Classification of Hyperspectral Images Using MRS Object-Guided Morphological Profiles

Journal of Imaging ◽

10.3390/jimaging6110114 ◽

2020 ◽

Vol 6 (11) ◽

pp. 114

Author(s):

Alim Samat ◽

Erzhu Li ◽

Sicong Liu ◽

Zelang Miao ◽

Wei Wang

Keyword(s):

Classification Accuracy ◽

Hyperspectral Image ◽

Hyperspectral Images ◽

Ensemble Classifiers ◽

Structural Elements ◽

Adaptive Boosting ◽

Spatial Feature ◽

Spatial Classification ◽

Multiresolution Segmentation

In spectral-spatial classification of hyperspectral image tasks, the performance of conventional morphological profiles (MPs) that use a sequence of structural elements (SEs) with predefined sizes and shapes could be limited by mismatching all the sizes and shapes of real-world objects in an image. To overcome such limitation, this paper proposes the use of object-guided morphological profiles (OMPs) by adopting multiresolution segmentation (MRS)-based objects as SEs for morphological closing and opening by geodesic reconstruction. Additionally, the ExtraTrees, bagging, adaptive boosting (AdaBoost), and MultiBoost ensemble versions of the extremely randomized decision trees (ERDTs) are introduced and comparatively investigated for spectral-spatial classification of hyperspectral images. Two hyperspectral benchmark images are used to validate the proposed approaches in terms of classification accuracy. The experimental results confirm the effectiveness of the proposed spatial feature extractors and ensemble classifiers.

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Meta-XGBoost for Hyperspectral Image Classification Using Extended MSER-Guided Morphological Profiles

Remote Sensing ◽

10.3390/rs12121973 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1973

Author(s):

Alim Samat ◽

Erzhu Li ◽

Wei Wang ◽

Sicong Liu ◽

Cong Lin ◽

...

Keyword(s):

Random Forest ◽

Image Classification ◽

Classification Accuracy ◽

Regression Tree ◽

Superior Performance ◽

Gradient Boosting ◽

Support Vector ◽

Adaptive Boosting ◽

Extreme Gradient Boosting

To investigate the performance of extreme gradient boosting (XGBoost) in remote sensing image classification tasks, XGBoost was first introduced and comparatively investigated for the spectral-spatial classification of hyperspectral imagery using the extended maximally stable extreme-region-guided morphological profiles (EMSER_MPs) proposed in this study. To overcome the potential issues of XGBoost, meta-XGBoost was proposed as an ensemble XGBoost method with classification and regression tree (CART), dropout-introduced multiple additive regression tree (DART), elastic net regression and parallel coordinate descent-based linear regression (linear) and random forest (RaF) boosters. Moreover, to evaluate the performance of the introduced XGBoost approach with different boosters, meta-XGBoost and EMSER_MPs, well-known and widely accepted classifiers, including support vector machine (SVM), bagging, adaptive boosting (AdaBoost), multi class AdaBoost (MultiBoost), extremely randomized decision trees (ExtraTrees), RaF, classification via random forest regression (CVRFR) and ensemble of nested dichotomies with extremely randomized decision tree (END-ERDT) methods, were considered in terms of the classification accuracy and computational efficiency. The experimental results based on two benchmark hyperspectral data sets confirm the superior performance of EMSER_MPs and EMSER_MPs with mean pixel values within region (EMSER_MPsM) compared to that for morphological profiles (MPs), morphological profile with partial reconstruction (MPPR), extended MPs (EMPs), extended MPPR (EMPPR), maximally stable extreme-region-guided morphological profiles (MSER_MPs) and MSER_MPs with mean pixel values within region (MSER_MPsM) features. The proposed meta-XGBoost algorithm is capable of obtaining better results than XGBoost with the CART, DART, linear and RaF boosters, and it could be an alternative to the other considered classifiers in terms of the classification of hyperspectral images using advanced spectral-spatial features, especially from generalized classification accuracy and model training efficiency perspectives.

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IMPLEMENTATION OF ENSEMBLE TECHNIQUES FOR DIARRHEA CASES CLASSIFICATION OF UNDER-FIVE CHILDREN IN INDONESIA

JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) ◽

10.33480/jitk.v6i2.1935 ◽

2021 ◽

Vol 6 (2) ◽

pp. 175-180

Author(s):

Andriansyah Muqiit Wardoyo Saputra ◽

Arie Wahyu Wijayanto

Keyword(s):

Data Analysis ◽

Imbalanced Data ◽

Classification Performance ◽

Adaptive Boosting ◽

Under Five ◽

Under Five Children ◽

Analysis Process ◽

Ensemble Techniques ◽

Lack Of Information

Diarrhea is an endemic disease in Indonesia with symptoms of three or more defecations with the consistency of liquid stool. According to WHO, diarrhea is the second largest contributor to the death of under-five children. Data and cases of children under five years who have diarrhea are very difficult to find, so the data analysis process becomes difficult due to the lack of information obtained. Difficulties in the data analysis process can be overcome by rebalancing, so the category ratios are balanced. The method that is popularly used is SMOTE. To solve imbalanced data and improve classification performance, this study implements the combination of SMOTE with several ensemble techniques in diarrhea cases of under-five children in Indonesia. Ensemble models that are used in this study are Random Forest, Adaptive Boosting, and XGBoost with Decision Tree as a baseline method. The results show that all SMOTE-based methods demonstrate a competitive performance whereas SMOTE-XGB gains a slightly higher accuracy (0.88), precision (0.96), and f1-score (0.86). The implementation of the SMOTE strategy improved the recall, precision, and f1-score metrics and give higher AUC of all methods (DT, RF, ADA, and XGB). This study is useful to solve the imbalanced problems in official statistics data provided by BPS Statistics Indonesia

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Learning Discriminating Features for Gender Recognition of Real World Faces

International Journal of Image and Graphics ◽

10.1142/s0219467814500119 ◽

2014 ◽

Vol 14 (03) ◽

pp. 1450011 ◽

Cited By ~ 1

Author(s):

Haider Ali ◽

Umair Ullah Tariq ◽

Muhammad Abid

Keyword(s):

Real World ◽

Local Binary Pattern ◽

Recognition Rate ◽

Support Vector ◽

Gender Classification ◽

Gender Recognition ◽

World Systems ◽

Adaptive Boosting ◽

Constrained Conditions

The automatic gender recognition of faces has many applications, for example surveillance, targeted advertisement and human computer interaction, etc. Humans have the ability to accurately determine the gender from faces, however, for a machine, it is a difficult task. Many studies have targeted this problem, but most of these studies have used images taken under constrained conditions. In Real-world systems have to process images with wide variations in lighting and pose that makes the classification task very challenging. We have analyzed the gender classification of real world faces. Faces from images are detected, aligned and represented using local binary pattern histograms. Adaptive boosting selects the discriminating features and boosted LBP features are used to train a support vector machine that provides a recognition rate of 95.5%.

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