Ensemble Of Weak Classifiers Research Articles

AUTOMATIC ROCK IDENTIFICATION FROM CORE PHOTOS USING MODERN MACHINE LEARNING METHODS

Layer-by-layer description of the core is performed to understand the regularities of the structure of the geological section, predict the development of reservoirs, clarify stratigraphic boundaries and obtain calculation parameters for assessing hydrocarbon reserves. In this case, the name of the breed is one of the key parameters determined in the layer-by-layer description. This paper presents a comparative analysis of two approaches to determining the breed using machine learning methods: based on graphical identifiers and convolutional neural networks. The original sample contained photographs of core samples from the Tyumenskaya suite fields (8 fields, 15 wells, more than 2 km of core) under daylight. For the analysis, 4 main classes of rocks (siltstones, mudstones, sandstones, coals) were selected. For these rocks, windows of 5 × 5 cm were formed and compressed to 299 × 299 pixels. The total sample exceeded 90,000 windows: 70% — training sample (60,359 windows) and 30% — test (31,140 windows). The training and test samples contain photographs of core samples from different fields. The comparison was made between convolutional neural networks (ResNet, ResNeXt, Inception, etc.) and a classifier (such as XGBoost) based on graphic identifiers of two types: color (average color, dominant colors) and texture (entropy, Euler’s number, contrast, dissimilarity, uniformity, energy, correlation). According to the results of the experiments, the model based on convolutional neural networks turned out to be more sensitive to implicit features and made it possible to reduce the error in the weighted average f1-measure with respect to the ensemble of weak classifiers by 12.5% on the test sample even without optimization of hyperparameters. Thus, we can conclude that the model based on convolutional neural networks is more sensitive to implicit features that are difficult to extract using known graphic identifiers. On the other hand, the approach based on graphic identifiers and an ensemble of weak classifiers can be used without specialized computing power (video cards).

Detection of medications associated with Alzheimer's disease using ensemble methods and cooperative game theory

ObjectiveTo study the feasibility of evaluating feature importance with Shapley Values and ensemble methods in the context of pharmacoepidemiology and medication safety. MethodsWe detected medications associated with Alzheimer's disease (AD) by examining the additive feature attribution with combined approach of Gradient Boosting and Shapley Values in the Medication use and Alzheimer's disease (MEDALZ) study, a nested case-control study of 70,719 verified AD cases in Finland. Our methodological approach is to do binary classification using Gradient boosting (an ensemble of weak classifiers) in a supervised learning manner. Then we apply Shapley Values (from cooperative game theory) to analyze how feature combinations affect the classification result. Medication use with a five to one year time-window before AD diagnosis was ascertained from Prescription register. ResultsAntipsychotics with low or medium dose, antidepressants with medium to high dose, and cardiovascular medications with medium to high dose were identified as the contributing features for separating cases with AD from controls. Medium to high amount of irregularity in the purchase pattern were an indicating feature for separating AD cases from controls. The similarity of medication purchases between AD cases and controls made the feature evaluation challenging. ConclusionsThe combined approach of Gradient Boosting and feature evaluation with Shapley Values identified features that were consistent with findings from previous hypothesis-driven studies. Additionally, the results from the additive feature attribution identified new candidates for future studies on AD risk factors. Our approach also shows promise for studies based on observational studies, where feature identification and interactions in populations are of interest; and the applicability of using Shapley Values for evaluating feature relevance in pattern recognition tasks.

A comparative study of classifier ensembles for detecting inactive learner in university

Prediction of undesirable learner's behaviours is an important issue in the distance learning system as well as the conventional university. This paper is devoted to benchmark ensemble of weak classifiers (decision tree, random forest, logistic regression, and CART) against single classifier models to predict inactive student. Two real-world datasets were obtained from a distance learning system and a computer science college in Indonesia. To evaluate the performance of the classifier ensembles, several performance metrics such as average accuracy, precision, recall, fall-out, F1, and area under ROC curve (AUC) value were involved. Our experiments reveal that classifier ensembles outperform single classifier in all evaluation metrics. This study contributes to the literature on making a comparative study of ensemble learners in the purview of educational data mining.

Identification of Colorectal Cancer Using Near-Infrared Spectroscopy and Adaboost with Decision Stump

ABSTRACTRapid and objective detection of cancer is crucial for successful treatment. Near-infrared (NIR) spectroscopy is a vibrational technique capable of optically probing molecular changes associated with disease. The purpose of this study was to explore NIR spectroscopy for discriminating cancer from normal colorectal tissues. A total of 110 tissue samples from patients who underwent operations were characterized in this study. The popular ensemble technique AdaBoost was used to construct the diagnostic model. A decision stump was used as the weak learning algorithm. Adaboost with decision stump, an ensemble of weak classifiers, was compared with the most suitable single model, a strong classifier. Only the 20 most significant variables were selected as inputs for the model based on measured defined variable importance. Using an independent test set, the single strong classifier provided diagnostic accuracy of 89.1%, sensitivity of 100%, and specificity of 78.6%, whereas the ensemble of weak stumps provided accuracy of 96.3%, sensitivity of 96.3%, and specificity of 96.3% for distinguishing cancer from normal colorectal tissues. Therefore, NIR spectroscopy in combination with AdaBoost with decision stumps has demonstrated potential for rapid and objective diagnosis of colorectal cancer.

판별 함수를 이용한 문턱치 선정에 의한 약분류기 개선

In this paper, we propose a quadratic discriminant analysis based approach for improving the discriminating strength of weak classifiers based on simple Haar-like features that were used in the Viola-Jones object detection framework. Viola and Jones built a strong classifier using a boosted ensemble of weak classifiers. However, their single threshold (or decision boundary) based weak classifier is sub-optimal and too weak for efficient discrimination between object class and background. A quadratic discriminant analysis based approach is presented which leads to hyper-quadric boundary between the object class and background class, thus realizing multiple thresholds based weak classifiers. Experiments carried out for car detection using 1000 positive and 3000 negative images for training, and 500 positive and 500 negative images for testing show that our method yields higher classification performance with fewer classifiers than single threshold based weak classifiers.

Local rotation-based ensemble

Many data analysis problems involve an investigation of relationships between attributes in heterogeneous databases, where different prediction models can be more appropriate for different regions. We propose a technique of local rotation-based ensemble of weak classifiers. In order to determine rotation forests, we identify local regions having similar characteristics and then build local classification experts on each of these regions describing the relationship between the data characteristics and the target class. We performed a comparison with other well-known combining methods using weak classifiers as based learners, on standard benchmark datasets and we took better accuracy.

Ensemble Tracking

We consider tracking as a binary classification problem, where an ensemble of weak classifiers is trained online to distinguish between the object and the background. The ensemble of weak classifiers is combined into a strong classifier using AdaBoost. The strong classifier is then used to label pixels in the next frame as either belonging to the object or the background, giving a confidence map. The peak of the map and, hence, the new position of the object, is found using mean shift. Temporal coherence is maintained by updating the ensemble with new weak classifiers that are trained online during tracking. We show a realization of this method and demonstrate it on several video sequences.