Real AdaBoost Research Articles

Classically boosted network embeddings

Abstract Network embeddings are a popular and effective pre-processing step when performing machine learning with network data. We demonstrate that standard boosting techniques, AdaBoost and Real AdaBoost can be applied to network embedding techniques to increase performance, particularly in terms of link prediction on test data in a cross-validation context. These approaches produce results competitive with other state-of-the-art embedding approaches when applied to a number of empirical networks. Additionally, we show on simulated data that Real AdaBoost can de-aggregate some networks, wherein networks created by two independent latent features can have those separate latent features inferred by different boosted rounds. Further analysis of the performance of these boosted methods shows that they retain the characteristic robustness to over-fitting as boosting methods in classical settings.

A Radiomic-based Machine Learning Algorithm to Reliably Differentiate Benign Renal Masses from Renal Cell Carcinoma

BackgroundA substantial proportion of patients undergo treatment for renal masses where active surveillance or observation may be more appropriate. ObjectiveTo determine whether radiomic-based machine learning platforms can distinguish benign from malignant renal masses. Design, setting, and participantsA prospectively maintained single-institutional renal mass registry was queried to identify patients with a computed tomography–proven clinically localized renal mass who underwent partial or radical nephrectomy. InterventionRadiomic analysis of preoperative scans was performed. Clinical and radiomic variables of importance were identified through decision tree analysis, which were incorporated into Random Forest and REAL Adaboost predictive models. Outcome measurements and statistical analysisThe primary outcome was the degree of congruity between the virtual diagnosis and final pathology. Subanalyses were performed for small renal masses and patients who had percutaneous renal mass biopsies as part of their workup. Receiver operating characteristic curves were used to evaluate each model’s discriminatory function. Results and limitationsA total of 684 patients met the selection criteria. Of them, 76% had renal cell carcinoma; 57% had small renal masses, of which 73% were malignant. Predictive modeling differentiated benign pathology from malignant with an area under the curve (AUC) of 0.84 (95% confidence interval [CI] 0.79–0.9). In small renal masses, radiomic analysis yielded a discriminatory AUC of 0.77 (95% CI 0.69–0.85). When negative and nondiagnostic biopsies were supplemented with radiomic analysis, accuracy increased from 83.3% to 93.4%. ConclusionsRadiomic-based predictive modeling may distinguish benign from malignant renal masses. Clinical factors did not substantially improve the diagnostic accuracy of predictive models. Enhanced diagnostic predictability may improve patient selection before surgery and increase the utilization of active surveillance protocols. Patient summaryNot all kidney tumors are cancerous, and some can be watched. We evaluated a new method that uses radiographic features invisible to the naked eye to distinguish benign masses from true cancers and found that it can do so with acceptable accuracy.

PD41-02 RADIOMIC-BASED “VIRTUAL BIOPSY” RELIABLY DIFFERENTIATES BENIGN FROM MALIGNANT RENAL MASSES

You have accessJournal of UrologySurgical Technology & Simulation: Instrumentation & Technology II (PD41)1 Sep 2021PD41-02 RADIOMIC-BASED “VIRTUAL BIOPSY” RELIABLY DIFFERENTIATES BENIGN FROM MALIGNANT RENAL MASSES Nima Nassiri, Marissa Maas, Giovanni Cacciamani, Bino Varghese, Darryl Hwang, Assad Oberai, Xiaomeng Lei, Mihir Desai, Monish Aron, Steven Cen, Vinay Duddalwar, and Inderbir Gill Nima NassiriNima Nassiri More articles by this author , Marissa MaasMarissa Maas More articles by this author , Giovanni CacciamaniGiovanni Cacciamani More articles by this author , Bino VargheseBino Varghese More articles by this author , Darryl HwangDarryl Hwang More articles by this author , Assad OberaiAssad Oberai More articles by this author , Xiaomeng LeiXiaomeng Lei More articles by this author , Mihir DesaiMihir Desai More articles by this author , Monish AronMonish Aron More articles by this author , Steven CenSteven Cen More articles by this author , Vinay DuddalwarVinay Duddalwar More articles by this author , and Inderbir GillInderbir Gill More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002051.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: A substantial proportion of patients undergo treatment for renal masses where active surveillance or observation may be more appropriate. We sought to determine whether a radiomic-based artificial intelligence (AI) platform that incorporates clinical variables can accurately differentiate benign renal masses from renal cell carcinoma (RCC). METHODS: We performed a cross-sectional analysis of a prospectively collected and maintained registry of patients with renal masses in accordance with the STROBE Statement for observational studies. All participants had a clinically-localized renal mass visualized with multiphase computed-tomography (CT) before robotic, laparoscopic, or open partial or radical nephrectomy. Radiomic analysis of pre-operative CT scans, specifically CT shape and texture analysis, was performed. 10-fold cross-validation was performed to overcome heterogeneities in radiomic analysis. Clinical and radiomic variables of interest (VOIs) were identified through decision tree analysis and incorporated into machine-learning Random Forest and REAL Adaboost predictive models. The primary outcome was the diagnostic capacity of the predictive model to discriminate benign renal masses from RCC. Secondary outcomes included a subanalysis for small renal masses (SRMs) and for patients who underwent percutaneous renal mass biopsy. RESULTS: 684 patients with a renal mass identified on CT imaging underwent radical or partial nephrectomy. Of the patients, 76% had RCC; 57% had an SRM, of which 73% were malignant. REAL Adaboost predictive modeling differentiated benign pathology from RCC with an AUC of 0.84 (95% C.I. 0.79-0.9) (Figure 1A). In SRMs, radiomic analysis yielded a discriminatory AUC of 0.77 (95%. C.I. 0.69-0.85) (Figure 1B). When negative and non-diagnostic biopsies were supplemented with the results of radiomic analysis, accuracy increased from 83.3% to 93.4%. CONCLUSIONS: Radiomic-based predictive modeling produces a “virtual biopsy” that may discriminate benign renal masses from RCC. Enhanced diagnostic predictability may preclude surgical intervention and increase the utility of active surveillance protocols. Source of Funding: no disclosures © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e680-e681 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Nima Nassiri More articles by this author Marissa Maas More articles by this author Giovanni Cacciamani More articles by this author Bino Varghese More articles by this author Darryl Hwang More articles by this author Assad Oberai More articles by this author Xiaomeng Lei More articles by this author Mihir Desai More articles by this author Monish Aron More articles by this author Steven Cen More articles by this author Vinay Duddalwar More articles by this author Inderbir Gill More articles by this author Expand All Advertisement Loading ...

Fuzzy OWL-Boost: Learning fuzzy concept inclusions via real-valued boosting

OWL ontologies are nowadays a quite popular way to describe structured knowledge in terms of classes, relations among classes and class instances. In this paper, given an OWL ontology and a target class T, we address the problem of learning fuzzy concept inclusion axioms that describe sufficient conditions for being an individual instance of T (and to which degree). To do so, we present Fuzzy OWL-Boost that relies on the Real AdaBoost boosting algorithm adapted to the (fuzzy) OWL case. We illustrate its effectiveness by means of an experimentation with several ontologies.

Spatial modeling of snow avalanche susceptibility using hybrid and ensemble machine learning techniques

In this study, snow avalanche susceptibility maps of Uzungöl Basin, which is a special environmental protected zone located in Eastern Black Sea Basin of Turkeywere conducted. For this, the belief function values of snow avalanche conditioning factors extracted from snow avalanche inventory data exploring geographical information system software were used. Classes of snow avalanche conditioning factors based on J48, random tree (RT), random forest (RF), and AdaBoost M1, Bagging, Real AdaBoost, and Random Subspace ensemble models of J48, RT, and RF models were implemented. Then snow avalanche susceptibility maps of each model were generated based on the combination rule of Dempster-Shafer theory. The model predictions were validated using a receiver operating characteristics curve, seed cell area index, and statistical tests. AdaBoost M1 ensemble of RT model was found to be the best performing results. However, ensemble models did not improve the prediction results unexpectedly. It can be expected that findings of this study will lead to the implementation of dynamic snow avalanche modeling studies on avalanche paths highlighted in the prepared maps, planning of land-use change projections, controlling snow avalanche movement, and mitigating the risks of loss of life and possible snow avalanches.

Landslide susceptibility mapping using state-of-the-art machine learning ensembles

This study propose a new approach through which the landslide susceptibility in Quang Nam (Vietnam) will be estimated using the best model among the following algorithms: Decision Table (DT), Naïve Bayes (NB), Decision Table - Naïve Bayes (DTNB), Bagging Ensemble, Cascade Generalization Ensemble, Dagging Ensemble, Decorate Ensemble, MultiBoost Ensemble, MultiScheme Ensemble, Real Ada Boost Ensemble, Rotation Forest Ensemble, Random Sub Space Ensemble. In this regard, a map with 1130 landslide, was created and further partitioned into training (70%) and testing (30%) locations. The correlation-based features selections (CFS) method was used to select a number of 15 landslide influencing factors. Landslide locations, included in the training sample, and the landslide predictors were used as input data in order to run the above mentioned models. Kappa index, Accuracy (%) and ROC curve were employed to estimate the model’s performance and to test the outcomes provided by the models. Among the eleven machine learning algorithms, Random Sub Space Decision Table Naïve Bayes (RSSDTNB) was the most performant model with an AUC = 0.839, Accuracy = 76.55% and Kappa Index = 0.531. Therefore, this algorithm was involved in the estimation of landslide susceptibility. The Success Rate (AUC = 0.815) and Prediction Rate (AUC = 0.826) revealed the achievement of high-quality results.

Modeling Spatial Flood using Novel Ensemble Artificial Intelligence Approaches in Northern Iran

The uncertainty of flash flood makes them highly difficult to predict through conventional models. The physical hydrologic models of flash flood prediction of any large area is very difficult to compute as it requires lot of data and time. Therefore remote sensing data based models (from statistical to machine learning) have become highly popular due to open data access and lesser prediction times. There is a continuous effort to improve the prediction accuracy of these models through introducing new methods. This study is focused on flash flood modeling through novel hybrid machine learning models, which can improve the prediction accuracy. The hybrid machine learning ensemble approaches that combine the three meta-classifiers (Real AdaBoost, Random Subspace, and MultiBoosting) with J48 (a tree-based algorithm that can be used to evaluate the behavior of the attribute vector for any defined number of instances) were used in the Gorganroud River Basin of Iran to assess flood susceptibility (FS). A total of 426 flood positions as dependent variables and a total of 14 flood conditioning factors (FCFs) as independent variables were used to model the FS. Several threshold-dependent and independent statistical tests were applied to verify the performance and predictive capability of these machine learning models, such as the receiver operating characteristic (ROC) curve of the success rate curve (SRC) and prediction rate curve (PRC), efficiency (E), root-mean square-error (RMSE), and true skill statistics (TSS). The valuation of the FCFs was done using AdaBoost, frequency ratio (FR), and Boosted Regression Tree (BRT) models. In the flooding of the study area, altitude, land use/land cover (LU/LC), distance to stream, normalized differential vegetation index (NDVI), and rainfall played important roles. The Random Subspace J48 (RSJ48) ensemble method with an area under the curve (AUC) of 0.931 (SRC), 0.951 (PRC), E of 0.89, sensitivity of 0.87, and TSS of 0.78, has become the most effective ensemble in predicting the FS. The FR technique also showed good performance and reliability for all models. Map removal sensitivity analysis (MRSA) revealed that the FS maps have the highest sensitivity to elevation. Based on the findings of the validation methods, the FS maps prepared using the machine learning ensemble techniques have high robustness and can be used to advise flood management initiatives in flood-prone areas.

Multipose Face Recognition-Based Combined Adaptive Deep Learning Vector Quantization.

Multipose face recognition system is one of the recent challenges faced by the researchers interested in security applications. Different researches have been introduced discussing the accuracy improvement of multipose face recognition through enhancing the face detector as Viola-Jones, Real Adaboost, and Cascade Object Detector while others concentrated on the recognition systems as support vector machine and deep convolution neural networks. In this paper, a combined adaptive deep learning vector quantization (CADLVQ) classifier is proposed. The proposed classifier has boosted the weakness of the adaptive deep learning vector quantization classifiers through using the majority voting algorithm with the speeded up robust feature extractor. Experimental results indicate that, the proposed classifier provided promising results in terms of sensitivity, specificity, precision, and accuracy compared to recent approaches in deep learning, statistical, and classical neural networks. Finally, the comparison is empirically performed using confusion matrix to ensure the reliability and robustness of the proposed system compared to the state-of art.

SVM-Boosting based on Markov resampling: Theory and algorithm

In this article we introduce the idea of Markov resampling for Boosting methods. We first prove that Boosting algorithm with general convex loss function based on uniformly ergodic Markov chain (u.e.M.c.) examples is consistent and establish its fast convergence rate. We apply Boosting algorithm based on Markov resampling to Support Vector Machine (SVM), and introduce two new resampling-based Boosting algorithms: SVM-Boosting based on Markov resampling (SVM-BM) and improved SVM-Boosting based on Markov resampling (ISVM-BM). In contrast with SVM-BM, ISVM-BM uses the support vectors to calculate the weights of base classifiers. The numerical studies based on benchmark datasets show that the proposed two resampling-based SVM Boosting algorithms for linear base classifiers have smaller misclassification rates, less total time of sampling and training compared to three classical AdaBoost algorithms: Gentle AdaBoost, Real AdaBoost, Modest AdaBoost. In addition, we compare the proposed SVM-BM algorithm with the widely used and efficient gradient Boosting algorithm-XGBoost (eXtreme Gradient Boosting), SVM-AdaBoost and present some useful discussions on the technical parameters.

GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models

Landslides have caused huge economic and human losses in China. Mapping of landslide susceptibility is an important tool to prevent and control landslide disasters. The purpose of this study is to make use of a hybrid machine learning approach by combining the reduced-error pruning trees (Rept) with a series of ensemble techniques (Bagging, Dagging, and Real Adaboost) and compare the performance of each combination for landslide susceptibility modeling. The combination of Rept model and Real Adaboost (RRept)is a novel application in the field of landslide susceptibility. Firstly, a landslide inventory map was prepared with 298 determined historical landslides events in the study area, 209 landslides (70%) were randomly selected for the training dataset and the remaining 89 landslides (30%) were used for validation dataset. On this basis, 16 landslide influencing factors were included in the landslide susceptibility evaluation (slope angle, elevation, slope aspect, sediment transport index (STI), topographical wetness index (TWI), stream power index (SPI), profile curvature, plan curvature, distance to rivers, distance to roads, distance to faults, soil, normalized difference vegetation index (NDVI), landuse, lithology and rainfall). Secondly, the correlation attribute evaluation (CAE) method was used to select the most important factors for the proposed landslide susceptibility model. The results show that all the factors contribute to the occurrence of landslide. Slope angle, Landuse, Elevation, Distance to roads, Soil and Lithology have the greatest influence on the occurrence of landslide. The receiver operating characteristics (ROC), standard error (SE), 95% confidence interval and mean absolute error (MAE) were then used to validate and compare the performance of the model. The best model should have the largest AUC value, the smallest SE, the narrowest 95% CI and the smallest MAE. The results show that the three hybrid models perform better than the Rept model alone. For the training data set, the RRept model has highest AUC value (0.927), the smallest SE (0.121), the narrowest 95% confidence interval (0.898–0.95) and the lowest MAE (0.20). For the validation data set, the RRept model has the highest AUC value (0.745), the narrowest 95% confidence interval (0.674–0.807) and the lowest MAE (0.33). The RRept has the highest predictive power for landslide susceptibility evaluation. The results show that a hybrid method improves the prediction ability of the base Rept model. In addition, the RRept model is a promising comprehensive model that can be applied to landslide susceptibility mapping.

Boosting algorithms for network intrusion detection: A comparative evaluation of Real AdaBoost, Gentle AdaBoost and Modest AdaBoost

Computer networks have been experienced ever-increasing growth since they play a critical role in different aspects of human life. Regarding the vulnerabilities of computer networks, they should be monitored regularly to detect intrusions and attacks by using high-performance Intrusion Detection Systems (IDSs). IDSs try to differentiate between normal and abnormal behaviors to recognize intrusions. Due to the complex behavior of malicious entities, it is crucially important to adopt machine learning methods for intrusion detection with a fine performance and low time complexity. Boosting approach is considered as a way to deal with this challenge. In this paper, we prepare a clear summary of the latest progress in the context of intrusion detection methods, present a technical background on boosting, and demonstrate the ability of the three well-known boosting algorithms (Real Adaboost, Gentle Adaboost, and Modest Adaboost) as IDSs by using five IDS public benchmark datasets. The results show that the Modest AdaBoost has a higher error rate compared to Gentle and Real AdaBoost in IDSs. Besides, in the case of IDSs, Gentle and Real AdaBoost show the same performance as they have about 70% lower error rates compared to Modest Adaboost, however, Modest AdaBoost is about 7% faster than them. In addition, as IDSs need to retrain the model frequently, the results show that Modest AdaBoost has a much lower performance than Gentle and Real AdaBoost in case of error rate stability.

Novel Ensemble Landslide Predictive Models Based on the Hyperpipes Algorithm: A Case Study in the Nam Dam Commune, Vietnam

Development of landslide predictive models with strong prediction power has become a major focus of many researchers. This study describes the first application of the Hyperpipes (HP) algorithm for the development of the five novel ensemble models that combine the HP algorithm and the AdaBoost (AB), Bagging (B), Dagging, Decorate, and Real AdaBoost (RAB) ensemble techniques for mapping the spatial variability of landslide susceptibility in the Nam Dan commune, Ha Giang province, Vietnam. Information on 76 historical landslides and ten geo-environmental factors (slope degree, slope aspect, elevation, topographic wetness index, curvature, weathering crust, geology, river density, fault density, and distance from roads) were used for the construction of the training and validation datasets that are the prerequisites for building and testing the proposed models. Using different performance metrics (i.e., the area under the receiver operating characteristic curve (AUC), negative predictive value, positive predictive value, accuracy, sensitivity, specificity, root mean square error, and Kappa), we verified the proficiency of all five ensemble learning techniques in increasing the fitness and predictive powers of the base HP model. Based on the AUC values derived from the models, the ensemble ABHP model that yielded an AUC value of 0.922 was identified as the most efficient model for mapping the landslide susceptibility in the Nam Dan commune, followed by RABHP (AUC = 0.919), BHP (AUC = 0.909), Dagging-HP (AUC = 0.897), Decorate-HP (AUC = 0.865), and the single HP model (AUC = 0.856), respectively. The novel ensemble models proposed for the Nam Dan commune and the resultant susceptibility maps can aid land-use planners in the development of efficient mitigation strategies in response to destructive landslides.

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam.

The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.

Vehicle detection using histogram of oriented gradients and real adaboost

The vehicle detection system is an important technology because it has many applications in traffic fields such as traffic monitoring, counting the number of vehicles passing, calculating the speed of an oncoming vehicle, and so on. Histogram Of Oriented Gradients (HOG) is a feature descriptor used for object detection. HOG describes features based on some local histogram of gradient orientation weighted by gradient magnitude. Real AdaBoost is a learning algorithm which combined weak classifier into a strong classifier that represents the final output of the boosted classifier. This research aims to detect vehicles in the static image using a Histogram Of Oriented Gradients methods and Real Adaboost. The steps of vehicle detection are pra-processing, feature extraction process with HOG, and classification process with Real AdaBoost. The testing result shows that the system can detect vehicles with an accuracy level of 91.78 % from 292 testing images, from 257 images of the vehicle and 35 images of not vehicle.

A Framework for Schizophrenia EEG Signal Classification With Nature Inspired Optimization Algorithms

One of the severe and prolonged disorder of the human brain which disturbs the behavioral characteristics of an individual completely such as interruption in the thinking process and speech is schizophrenia. It is a manifestation of many symptoms such as hallucinations, functional deterioration, disorganized speech and hearing sounds and speeches that are non-existent. In this paper, a computerized approach based on optimization and classification is done to analyze the classification of schizophrenia from Electroencephalography (EEG) signals. As EEG can analyze a lot of brain disorders and is used to study the diseases of the brain in an in-depth manner, it can be used to analyze the schizophrenia EEG signals. In this paper, three feature extraction techniques are employed such as Partial Least Squares (PLS) Non linear Regression technique, Expectation Maximization based Principal Component Analysis (EM-PCA) technique and Isometric Mapping (Isomap) technique. The extracted features are further optimized with four optimization algorithms such as Flower Pollination algorithm, Eagle strategy using different evolution algorithm, Backtracking search optimization algorithm and Group search optimization algorithm. The optimized values are then classified with varied versions of both Adaboost classifier and Naive Bayesian Classifier. The individual results show that for normal cases, Isomap features when optimized with Backtracking search optimization algorithm and classified with Modest Adaboost classifier, a classification accuracy of 98.77% is obtained. The individual results show that for schizophrenia case, when Isomap features are optimized with Flower Pollination optimization algorithm and classified with Real Adaboost classifier, a classification accuracy of 98.77% is obtained.

Learning‐based method for lane detection using regionlet representation

Lane detection is an important enabling or enhancing technology for many intelligent applications. A marker line can be segmented into several image blocks, each of which contains lane marking in the centre. This study proposes a learning-based method for lane localisation via detecting and grouping such image blocks. The authors model the marking class using regionlet representation, in which each image block is regarded as a region and is represented by a group of regionlets. A region feature composed of the features extracted from the regionlets contributes a weak classifier. A cascade structure detector is then trained for lane detection. At early stages, it rejects as many negatives as possible. Each layer of the cascade detector is a strong classifier, which consists of several weak classifiers. A real AdaBoost algorithm is adopted to select the most discriminative features and to train the classifiers. Since the use of regionlet features allows desired performance with only a few weak classifiers and the dimensionality of the features is significantly reduced by principal component analysis, the computational burden of their algorithm is much lower than other learning-based methods. Experiment results demonstrate the computational efficiency and robustness of the method.

Pengaruh Image Engagement pada Aplikasi Parasit Malaria

The mosquito's parasite is a group of single-celled microorganisms in the plasmodium type that can cause malaria by attacking human blood cells. In this study, a parasitic detection application of Plasmodium Falciparum type at thropozoite, scizont and gametocyte stage was designed using Android Studio 2.2.2 and OpenCV 2.4.9 library. Image detection process begins with preprocessing, then feature extraction and classification phase using Haar Cascade Classifier, then the last stage 3 types of boost compared, namely Gentle Adaboost, Discrete Adaboost, and Real Adaboost. Image Enhancement is one of the preliminary processes in preprocessing that aims to clarify certain features or features of the image to be more easily analyzed carefully in the feature selection process. The results of this study prove that image enhancement can be used to improve image quality so that the information available on the image can be seen more clearly.

Predicting Stock Market Indices Using Classification Tools

Increasing interest has been shown in the use of classifiers to extract informative patterns from time series data generated by monitoring financial phenomena. This paper investigates data mining and pattern recognition methods in forecasting the movement of the Standard & Poor’s 500 index. We use functional forms of varying classifiers to predict financial time series data and to evaluate the performance of different classifiers. By using the time series ARIMA model, we forecast the Standard & Poor’s 500 index. Additionally, with the AdaBoost algorithm and its extensions, we compare the classifying accuracy rates of bagging and boosting models with several classifiers, such as support vector machines, k-nearest neighbor, the probabilistic neural network, and the classification and regression tree. Results indicate that the boosting classifier with real AdaBoost (exponential loss) best forecast the Standard & Poor’s 500 index movements. This result should be relevant to firms that want to predict the stock prices.

LIDAR を用いた歩行者検出のためのReal AdaBoost に基づく特徴選択

LIDAR を用いた歩行者検出のためのReal AdaBoost に基づく特徴選択

Approach toward extraction of sparse texture features and their application in robust two-level bare-hand detection

A practical two-level robust hand detection system is developed using the proposed sparse texture features and color–texture features. Traditional dense features such as a histogram of oriented gradients, Gabor, segmentation-based fractal texture analysis, and histogram features are dense in general, with high time-complexity, limiting their use in practical hand detection systems. However, if only the prominent edge or texture parameters of an image are processed, the time complexity of the system can be significantly reduced, along with an increase in performance. Performance of any practical system is most affected by the presence of spurious objects in the background. Proposed approaches use four efficient filtering techniques to extract salient regions of an image retaining significant object-related information, followed by extraction of texture features, as mentioned above. Therefore, in the first stage, 10 sparse variants of these existing texture features are extracted and assessed using five classification models, namely Naive Bayes, Real AdaBoost, Gentle AdaBoost, Modest AdaBoost, and SVM. In the second phase of the system, a two-level hand detection model is developed using the proposed texture and color–texture features using a sliding window-based SVM classification model. Experimental analysis shows that proposed sparse-based features are not only time efficient but also shows an improvement of 23.4% in the practical two-level detection system over the existing motion and skin filtering-based hand detection system.