A robust multi-class AdaBoost algorithm for mislabeled noisy data

Sun, Bo; Chen, Songcan; Wang, Jiandong; Chen, Haiyan

doi:10.1016/j.knosys.2016.03.024

Cited by 71 publications

(39 citation statements)

References 24 publications

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“…For this study, random forest (RF) and AdaBoost (AB) classifiers were used, due to their popularity and representativeness for respective groups of algorithms, based on bagging or boosting approaches respectively. In addition, the selected classifiers in their resistance to the presence of noisy observation data, with the RF classifier considered as one of the most resistant algorithms [3,9,27,28].…”

Section: Classification and Validationmentioning

confidence: 99%

“…The RF algorithm is commonly considered as largely resistant to noisy and mislabeled training data [3,9,27,28,42]. According to some studies, even features containing noise levels as high as 30% result in a decrease in Kappa accuracy in the range of 10%, which is considered a very moderate decrease [43].…”

Section: Impact Of the On-ground Reference Dataset Modification On Thmentioning

confidence: 99%

“…Additionally, the RF has a reputation for being relatively resistant to the presence of noise in training data [3,9,26,27]. Conversely, a number of references indicate AB has a lower resistance [28][29][30]. Apart from the commonly used accuracy measures, a visual assessment of the resulting maps was carried out to assess the results of classification.…”

Section: Introductionmentioning

confidence: 99%

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The t-SNE Algorithm as a Tool to Improve the Quality of Reference Data Used in Accurate Mapping of Heterogeneous Non-Forest Vegetation

Halladin-Dabrowska

Kania²,

Kopeć

2019

Remote Sensing

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Supervised classification methods, used for many applications, including vegetation mapping require accurate "ground truth" to be effective. Nevertheless, it is common for the quality of this data to be poorly verified prior to it being used for the training and validation of classification models. The fact that noisy or erroneous parts of the reference dataset are not removed is usually explained by the relatively high resistance of some algorithms to errors. The objective of this study was to demonstrate the rationale for cleaning the reference dataset used for the classification of heterogeneous non-forest vegetation, and to present a workflow based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm for the better integration of reference data with remote sensing data in order to improve outcomes. The proposed analysis is a new application of the t-SNE algorithm. The effectiveness of this workflow was tested by classifying three heterogeneous non-forest Natura 2000 habitats: Molinia meadows (Molinion caeruleae; code 6410), species-rich Nardus grassland (code 6230) and dry heaths (code 4030), employing two commonly used algorithms: random forest (RF) and AdaBoost (AB), which, according to the literature, differ in their resistance to errors in reference datasets. Polygons collected in the field (on-ground reference data) in 2016 and 2017, containing no intentional errors, were used as the on-ground reference dataset. The remote sensing data used in the classification were obtained in 2017 during the peak growing season by a HySpex sensor consisting of two imaging spectrometers covering spectral ranges of 0.4-0.9 µm (VNIR-1800) and 0.9-2.5 µm (SWIR-384). The on-ground reference dataset was gradually cleaned by verifying candidate polygons selected by visual interpretation of t-SNE plots. Around 40-50% of candidate polygons were ultimately found to contain errors. Altogether, 15% of reference polygons were removed. As a result, the quality of the final map, as assessed by the Kappa and F1 accuracy measures as well as by visual evaluation, was significantly improved. The global map accuracy increased by about 6% (in Kappa coefficient), relative to the baseline classification obtained using random removal of the same number of reference polygons.Data gathered based on the photointerpretation of a high-resolution orthophoto map are used less frequently [3]. Regardless of the method used to collect reference data, they may contain errors, even those acquired by authoritative sources [4]. Errors in the data may result from various factors, one of which being differences in the methods used for the identification of vegetation type when they come from different sources and when they were collected for different purposes [1]. In this case, the problem may arise from both a different interpretation of a given vegetation type by individual researchers, and the determination of vegetation units at different hierarchy levels (upper hierarchy level = higher level of generality). Errors in the reference data...

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Section: Classification and Validationmentioning

confidence: 99%

Section: Impact Of the On-ground Reference Dataset Modification On Thmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The t-SNE Algorithm as a Tool to Improve the Quality of Reference Data Used in Accurate Mapping of Heterogeneous Non-Forest Vegetation

Halladin-Dabrowska

Kania²,

Kopeć

2019

Remote Sensing

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“…The AdaBoost (adaptive boosting) algorithm [32,33] is one of the best-known Boost algorithms and has the advantage of being simple and efficient. The use of the AdaBoost learning algorithm makes it possible to acquire more feature values that better express the target object as learning progresses, so that an accurate and robust recognition algorithm can be created.…”

Section: Filtering Candidate Areas Using Learning Generallymentioning

confidence: 99%

Harmful Content Detection Based on Cascaded Adaptive Boosting

Jang

Lee

2018

Journal of Sensors

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Recently, it has become very easy to acquire various types of image contents through mobile devices with high-performance visual sensors. However, harmful image contents such as nude pictures and videos are also distributed and spread easily. Therefore, various methods for effectively detecting and filtering such image contents are being introduced continuously. In this paper, we propose a new approach to robustly detect the human navel area, which is an element representing the harmfulness of the image, using Haar-like features and a cascaded AdaBoost algorithm. In the proposed method, the nipple area of a human is detected first using the color information from the input image and the candidate navel regions are detected using positional information relative to the detected nipple area. Nonnavel areas are then removed from the candidate navel regions and only the actual navel areas are robustly detected through filtering using the Haar-like feature and the cascaded AdaBoost algorithm. The experimental results show that the proposed method extracts nipple and navel areas more precisely than the conventional method. The proposed navel area detection algorithm is expected to be used effectively in various applications related to the detection of harmful contents.

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“…Sun et al, 2016 [10] quoted a representative approach named noise-detection based AdaBoost (ND_AdaBoost) in order to improve the robustness of AdaBoost in the two-class classification scenario. In order to resolve the dilemma a robust multi-class AdaBoost algorithm (Rob_MulAda) is proposed by the authors whose key ingredients consist in a noise-detection based multi-class loss function and a new weight updating scheme.…”

Section: Recent Work On Adaboostmentioning

confidence: 99%

A Literature Review on Supervised Machine Learning Algorithms and Boosting Process

Praveena¹,

Jaiganesh²

2017

IJCA

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Data mining is one amid the core research areas in the field of computer science. Yet there is a knowledge data detection process helps the data mining to extract hidden information from the dataset there is a big scope of machine learning algorithms. Especially supervised machine learning algorithms gain extensive importance in data mining research. Boosting action is regularly helps the supervised machine learning algorithms for rising the predictive / classification veracity. This survey research article prefer two famous supervised machine learning algorithms that is decision trees and support vector machine and presented the recent research works carried out. Also recent improvement on Adaboost algorithms (boosting process) is also granted. From this survey research it is learnt that connecting supervised machine learning algorithm with boosting process increased prediction efficiency and there is a wide scope in this research element.

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A robust multi-class AdaBoost algorithm for mislabeled noisy data

Cited by 71 publications

References 24 publications

The t-SNE Algorithm as a Tool to Improve the Quality of Reference Data Used in Accurate Mapping of Heterogeneous Non-Forest Vegetation

The t-SNE Algorithm as a Tool to Improve the Quality of Reference Data Used in Accurate Mapping of Heterogeneous Non-Forest Vegetation

Harmful Content Detection Based on Cascaded Adaptive Boosting

A Literature Review on Supervised Machine Learning Algorithms and Boosting Process

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