scholarly journals Few-Shot Learning for Post-Earthquake Urban Damage Detection

2021 ◽  
Vol 14 (1) ◽  
pp. 40
Author(s):  
Eftychia Koukouraki ◽  
Leonardo Vanneschi ◽  
Marco Painho
Keyword(s):  
Damage Assessment ◽  
Binary Classification ◽  
Training Data ◽  
Classification Problems ◽  
Deep Convolutional Neural Networks ◽  
Damage Classification ◽  
Emergency Relief ◽  
Cost Sensitive Learning ◽  
Urban Structures ◽  
Address Data

Among natural disasters, earthquakes are recorded to have the highest rates of human loss in the past 20 years. Their unexpected nature has severe consequences on both human lives and material infrastructure, demanding urgent action to be taken. For effective emergency relief, it is necessary to gain awareness about the level of damage in the affected areas. The use of remotely sensed imagery is popular in damage assessment applications; however, it requires a considerable amount of labeled data, which are not always easy to obtain. Taking into consideration the recent developments in the fields of Machine Learning and Computer Vision, this study investigates and employs several Few-Shot Learning (FSL) strategies in order to address data insufficiency and imbalance in post-earthquake urban damage classification. While small datasets have been tested against binary classification problems, which usually divide the urban structures into collapsed and non-collapsed, the potential of limited training data in multi-class classification has not been fully explored. To tackle this gap, four models were created, following different data balancing methods, namely cost-sensitive learning, oversampling, undersampling and Prototypical Networks. After a quantitative comparison among them, the best performing model was found to be the one based on Prototypical Networks, and it was used for the creation of damage assessment maps. The contribution of this work is twofold: we show that oversampling is the most suitable data balancing method for training Deep Convolutional Neural Networks (CNN) when compared to cost-sensitive learning and undersampling, and we demonstrate the appropriateness of Prototypical Networks in the damage classification context.

scholarly journals Learning Diatoms Classification from a Dry Test Slide by Holographic Microscopy

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6353
Author(s):  
Pasquale Memmolo ◽  
Pierluigi Carcagnì ◽  
Vittorio Bianco ◽  
Francesco Merola ◽  
Andouglas Goncalves da Silva Junior ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Imaging Modality ◽  
Training Data ◽  
Classification Problems ◽  
Deep Convolutional Neural Networks ◽  
Live Diatoms ◽  
Freshwater Habitats ◽  
Commercial Glass ◽  
Holographic Microscopy

Diatoms are among the dominant phytoplankters in marine and freshwater habitats, and important biomarkers of water quality, making their identification and classification one of the current challenges for environmental monitoring. To date, taxonomy of the species populating a water column is still conducted by marine biologists on the basis of their own experience. On the other hand, deep learning is recognized as the elective technique for solving image classification problems. However, a large amount of training data is usually needed, thus requiring the synthetic enlargement of the dataset through data augmentation. In the case of microalgae, the large variety of species that populate the marine environments makes it arduous to perform an exhaustive training that considers all the possible classes. However, commercial test slides containing one diatom element per class fixed in between two glasses are available on the market. These are usually prepared by expert diatomists for taxonomy purposes, thus constituting libraries of the populations that can be found in oceans. Here we show that such test slides are very useful for training accurate deep Convolutional Neural Networks (CNNs). We demonstrate the successful classification of diatoms based on a proper CNNs ensemble and a fully augmented dataset, i.e., creation starting from one single image per class available from a commercial glass slide containing 50 fixed species in a dry setting. This approach avoids the time-consuming steps of water sampling and labeling by skilled marine biologists. To accomplish this goal, we exploit the holographic imaging modality, which permits the accessing of a quantitative phase-contrast maps and a posteriori flexible refocusing due to its intrinsic 3D imaging capability. The network model is then validated by using holographic recordings of live diatoms imaged in water samples i.e., in their natural wet environmental condition.

A Calibrated Multiclass Extension of AdaBoost

2011 ◽  
Vol 10 (1) ◽  
Author(s):  
Daniel B. Rubin
Keyword(s):  
Loss Function ◽  
Numerical Experiments ◽  
Binary Classification ◽  
Training Data ◽  
Statistical Interpretation ◽  
Classification Problems ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Empirical Risk ◽  
Benchmark Datasets

AdaBoost is a popular and successful data mining technique for binary classification. However, there is no universally agreed upon extension of the method for problems with more than two classes. Most multiclass generalizations simply reduce the problem to a series of binary classification problems. The statistical interpretation of AdaBoost is that it operates through loss-based estimation: by using an exponential loss function as a surrogate for misclassification loss, it sequentially minimizes empirical risk through fitting a base classifier to iteratively reweighted training data. While there are several extensions using loss-based estimation with multiclass base classifiers, these use multiclass versions of the exponential loss that are not classification calibrated: unless restrictions are placed on conditional class probabilities, it becomes possible to have optimal surrogate risk but poor misclassification risk. In this work, we introduce a new AdaBoost extension called AdaBoost.SL that does not reduce the problem into binary subproblems and that uses a classification-calibrated multiclass exponential loss function. Numerical experiments show the algorithm performs well on benchmark datasets.

scholarly journals Data Cleaning for Classification Using Misclassification Analysis

2010 ◽  
Vol 14 (3) ◽  
pp. 297-302 ◽  
Author(s):  
Piyasak Jeatrakul ◽  
◽  
Kok Wai Wong ◽  
Chun Che Fung
Keyword(s):  
Data Cleaning ◽  
Binary Classification ◽  
Good Alternative ◽  
Training Data ◽  
Classification Model ◽  
Data Sets ◽  
Pima Indians ◽  
Classification Problems ◽  
Data Set ◽  
Preprocessing Technique

In most classification problems, sometimes in order to achieve better results, data cleaning is used as a preprocessing technique. The purpose of data cleaning is to remove noise, inconsistent data and errors in the training data. This should enable the use of a better and representative data set to develop a reliable classification model. In most classification models, unclean data could sometime affect the classification accuracies of a model. In this paper, we investigate the use of misclassification analysis for data cleaning. In order to demonstrate our concept, we have used Artificial Neural Network (ANN) as the core computational intelligence technique. We use four benchmark data sets obtained from the University of California Irvine (UCI) machine learning repository to investigate the results from our proposed data cleaning technique. The experimental data sets used in our experiment are binary classification problems, which are German credit data, BUPA liver disorders, Johns Hopkins Ionosphere and Pima Indians Diabetes. The results show that the proposed cleaning technique could be a good alternative to provide some confidence when constructing a classification model.

scholarly journals A Novel Data Representation for Effective Learning in Class Imbalanced Scenarios

2018 ◽  
Author(s):  
Sri Harsha Dumpala ◽  
Rupayan Chakraborty ◽  
Sunil Kumar Kopparapu
Keyword(s):  
Class Imbalance ◽  
Test Sample ◽  
Data Representation ◽  
Machine Learning Algorithms ◽  
Majority Voting ◽  
Training Data ◽  
Classification Problems ◽  
Class Imbalance Problem ◽  
Cost Sensitive Learning ◽  
Two Samples

Class imbalance refers to the scenario where certain classes are highly under-represented compared to other classes in terms of the availability of training data. This situation hinders the applicability of conventional machine learning algorithms to most of the classification problems where class imbalance is prominent. Most existing methods addressing class imbalance either rely on sampling techniques or cost-sensitive learning methods; thus inheriting their shortcomings. In this paper, we introduce a novel approach that is different from sampling or cost-sensitive learning based techniques, to address the class imbalance problem, where two samples are simultaneously considered to train the classifier. Further, we propose a mechanism to use a single base classifier, instead of an ensemble of classifiers, to obtain the output label of the test sample using majority voting method. Experimental results on several benchmark datasets clearly indicate the usefulness of the proposed approach over the existing state-of-the-art techniques.

scholarly journals AUC-Maximizing Ensembles through Metalearning

2016 ◽  
Vol 12 (1) ◽  
pp. 203-218 ◽  
Author(s):  
Erin LeDell ◽  
Mark J. van der Laan ◽  
Maya Petersen
Keyword(s):  
Nonlinear Optimization ◽  
Roc Curve ◽  
Optimization Problem ◽  
Binary Classification ◽  
Optimization Algorithms ◽  
Training Data ◽  
Classification Problems ◽  
Nonlinear Optimization Problem ◽  
Auc Maximization ◽  
Super Learner

Abstract Area Under the ROC Curve (AUC) is often used to measure the performance of an estimator in binary classification problems. An AUC-maximizing classifier can have significant advantages in cases where ranking correctness is valued or if the outcome is rare. In a Super Learner ensemble, maximization of the AUC can be achieved by the use of an AUC-maximining metalearning algorithm. We discuss an implementation of an AUC-maximization technique that is formulated as a nonlinear optimization problem. We also evaluate the effectiveness of a large number of different nonlinear optimization algorithms to maximize the cross-validated AUC of the ensemble fit. The results provide evidence that AUC-maximizing metalearners can, and often do, out-perform non-AUC-maximizing metalearning methods, with respect to ensemble AUC. The results also demonstrate that as the level of imbalance in the training data increases, the Super Learner ensemble outperforms the top base algorithm by a larger degree.

A MULTICLASS EXTENSION TO THE BROWNBOOST ALGORITHM

2004 ◽  
Vol 18 (05) ◽  
pp. 905-931 ◽  
Author(s):  
ROSS A. MCDONALD ◽  
DAVID J. HAND ◽  
IDRIS A. ECKLEY
Keyword(s):  
Binary Classification ◽  
Simulated Data ◽  
Training Data ◽  
Data Sets ◽  
Base Line ◽  
Classification Problems ◽  
Undue Influence ◽  
Basic Algorithm ◽  
Class Noise ◽  
Boosting Algorithm

Brownboost is an adaptive, continuous time boosting algorithm based on the Boost-by-Majority (BBM) algorithm. Though it has been little studied at the time of writing, it is believed that it should prove especially robust with respect to noisy data sets. This would make it a very useful boosting algorithm for real-world applications. More familiar algorithms such as Adaboost, or its successor Logitboost, are known to be especially susceptible to overfitting the training data examples. This can lead to a poor generalization error in the presence of class noise, since weak hypotheses induced at later iterations to fit the noisy examples will tend to be given undue influence in the final combined hypothesis. Brownboost allows us to specify an expected base-line error rate in advance, corresponding to our prior beliefs about the proportion of noise in the training data, and thus avoid overfitting. The original derivation of Brownboost is restricted to binary classification problems. In this paper we propose a natural multiclass extension to the basic algorithm, incorporating error-correcting output codes and a multiclass gain measure. We test two-class and multiclass versions of the algorithm on a number of real and simulated data sets with artificial class noise, and show that Brownboost consistently outperforms Adaboost in these situations.

scholarly journals On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks

2020 ◽  
Vol 34 (04) ◽  
pp. 4739-4746
Author(s):  
Xiangrui Li ◽  
Xin Li ◽  
Deng Pan ◽  
Dongxiao Zhu
Keyword(s):  
Neural Networks ◽  
Visual Recognition ◽  
Binary Classification ◽  
Training Data ◽  
Necessary Condition ◽  
Deep Convolutional Neural Networks ◽  
Target Class ◽  
Benchmark Datasets ◽  
Negative Class ◽  
Multi Class Classification

Deep convolutional neural networks (CNNs) trained with logistic and softmax losses have made significant advancement in visual recognition tasks in computer vision. When training data exhibit class imbalances, the class-wise reweighted version of logistic and softmax losses are often used to boost performance of the unweighted version. In this paper, motivated to explain the reweighting mechanism, we explicate the learning property of those two loss functions by analyzing the necessary condition (e.g., gradient equals to zero) after training CNNs to converge to a local minimum. The analysis immediately provides us explanations for understanding (1) quantitative effects of the class-wise reweighting mechanism: deterministic effectiveness for binary classification using logistic loss yet indeterministic for multi-class classification using softmax loss; (2) disadvantage of logistic loss for single-label multi-class classification via one-vs.-all approach, which is due to the averaging effect on predicted probabilities for the negative class (e.g., non-target classes) in the learning process. With the disadvantage and advantage of logistic loss disentangled, we thereafter propose a novel reweighted logistic loss for multi-class classification. Our simple yet effective formulation improves ordinary logistic loss by focusing on learning hard non-target classes (target vs. non-target class in one-vs.-all) and turned out to be competitive with softmax loss. We evaluate our method on several benchmark datasets to demonstrate its effectiveness.

scholarly journals Improving Land Cover Classification Using Genetic Programming for Feature Construction

2021 ◽  
Vol 13 (9) ◽  
pp. 1623
Author(s):  
João E. Batista ◽  
Ana I. R. Cabral ◽  
Maria J. P. Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva
Keyword(s):  
Land Cover ◽  
Genetic Programming ◽  
Satellite Images ◽  
State Of The Art ◽  
Binary Classification ◽  
Feature Construction ◽  
Classification Problems ◽  
Construction Methods ◽  
Box Models

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.

scholarly journals Building Damage Detection Using U-Net with Attention Mechanism from Pre- and Post-Disaster Remote Sensing Datasets

2021 ◽  
Vol 13 (5) ◽  
pp. 905
Author(s):  
Chuyi Wu ◽  
Feng Zhang ◽  
Junshi Xia ◽  
Yichen Xu ◽  
Guoqing Li ◽  
...  
Keyword(s):  
Damage Assessment ◽  
Large Scale ◽  
Binary Classification ◽  
Open Data ◽  
Building Damage ◽  
Attention Mechanism ◽  
Large Scale Dataset ◽  
Data Program ◽  
The Impact ◽  
Post Disaster

The building damage status is vital to plan rescue and reconstruction after a disaster and is also hard to detect and judge its level. Most existing studies focus on binary classification, and the attention of the model is distracted. In this study, we proposed a Siamese neural network that can localize and classify damaged buildings at one time. The main parts of this network are a variety of attention U-Nets using different backbones. The attention mechanism enables the network to pay more attention to the effective features and channels, so as to reduce the impact of useless features. We train them using the xBD dataset, which is a large-scale dataset for the advancement of building damage assessment, and compare their result balanced F (F1) scores. The score demonstrates that the performance of SEresNeXt with an attention mechanism gives the best performance, with the F1 score reaching 0.787. To improve the accuracy, we fused the results and got the best overall F1 score of 0.792. To verify the transferability and robustness of the model, we selected the dataset on the Maxar Open Data Program of two recent disasters to investigate the performance. By visual comparison, the results show that our model is robust and transferable.

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