Coping with imbalanced training data for improved terrain prediction in autonomous outdoor robot navigation

2010 ◽  
Author(s):  
Michael J Procopio ◽  
Jane Mulligan ◽  
Greg Grudic
Keyword(s):  
Robot Navigation ◽  
Training Data ◽  
Imbalanced Training Data

scholarly journals Phonetic Variation Modeling and a Language Model Adaptation for Korean English Code-Switching Speech Recognition

2021 ◽  
Vol 11 (6) ◽  
pp. 2866
Author(s):  
Damheo Lee ◽  
Donghyun Kim ◽  
Seung Yun ◽  
Sanghun Kim
Keyword(s):  
Speech Recognition ◽  
Language Model ◽  
Reduction Rate ◽  
Code Switching ◽  
Training Data ◽  
Target Domain ◽  
Phonetic Variation ◽  
Language Model Adaptation ◽  
Imbalanced Training Data ◽  
Lm Adaptation

In this paper, we propose a new method for code-switching (CS) automatic speech recognition (ASR) in Korean. First, the phonetic variations in English pronunciation spoken by Korean speakers should be considered. Thus, we tried to find a unified pronunciation model based on phonetic knowledge and deep learning. Second, we extracted the CS sentences semantically similar to the target domain and then applied the language model (LM) adaptation to solve the biased modeling toward Korean due to the imbalanced training data. In this experiment, training data were AI Hub (1033 h) in Korean and Librispeech (960 h) in English. As a result, when compared to the baseline, the proposed method improved the error reduction rate (ERR) by up to 11.6% with phonetic variant modeling and by 17.3% when semantically similar sentences were applied to the LM adaptation. If we considered only English words, the word correction rate improved up to 24.2% compared to that of the baseline. The proposed method seems to be very effective in CS speech recognition.

Modeling of Cu-Au prospectivity in the Carajás mineral province (Brazil) through machine learning: Dealing with imbalanced training data

2020 ◽  
Vol 124 ◽  
pp. 103611 ◽  
Author(s):  
Elias Martins Guerra Prado ◽  
Carlos Roberto de Souza Filho ◽  
Emmanuel John M. Carranza ◽  
João Gabriel Motta
Keyword(s):  
Machine Learning ◽  
Training Data ◽  
Carajás Mineral Province ◽  
Imbalanced Training Data

Fuzzy Asymmetric Support Vector Machines

2012 ◽  
Vol 433-440 ◽  
pp. 7479-7486
Author(s):  
Rui Kong ◽  
Qiong Wang ◽  
Gu Yu Hu ◽  
Zhi Song Pan
Keyword(s):  
Support Vector Machines ◽  
Medical Diagnosis ◽  
Credit Card ◽  
Training Data ◽  
Support Vector ◽  
Imbalanced Datasets ◽  
Credit Card Fraud ◽  
Vector Machines ◽  
Imbalanced Training Data ◽  
Error Costs

Support Vector Machines (SVM) has been extensively studied and has shown remarkable success in many applications. However the success of SVM is very limited when it is applied to the problem of learning from imbalanced datasets in which negative instances heavily outnumber the positive instances (e.g. in medical diagnosis and detecting credit card fraud). In this paper, we propose the fuzzy asymmetric algorithm to augment SVMs to deal with imbalanced training-data problems, called FASVM, which is based on fuzzy memberships, combined with different error costs (DEC) algorithm. We compare the performance of our algorithm against these two algorithms, along with different error costs and regular SVM and show that our algorithm outperforms all of them.

scholarly journals The Impact of Imbalanced Training Data on Local Matching Learning of Ontologies

2019 ◽  
pp. 162-175
Author(s):  
Amir Laadhar ◽  
Faiza Ghozzi ◽  
Imen Megdiche ◽  
Franck Ravat ◽  
Olivier Teste ◽  
...  
Keyword(s):  
Training Data ◽  
Imbalanced Training Data ◽  
The Impact

scholarly journals Fast Extraction Method of High-Level Feature Using Random Forests from Imbalanced Training Data

2010 ◽  
Vol 64 (12) ◽  
pp. 1951-1955
Author(s):  
Yoshihiko Kawai ◽  
Hideki Sumiyoshi ◽  
Mahito Fujii ◽  
Masahiro Shibata ◽  
Noboru Babaguchi
Keyword(s):  
Random Forests ◽  
Extraction Method ◽  
Training Data ◽  
Imbalanced Training Data ◽  
High Level ◽  
Fast Extraction ◽  
High Level Feature

A binary PSO-based ensemble under-sampling model for rebalancing imbalanced training data

2021 ◽  
Author(s):  
Jinyan Li ◽  
Yaoyang Wu ◽  
Simon Fong ◽  
Antonio J. Tallón-Ballesteros ◽  
Xin-she Yang ◽  
...  
Keyword(s):  
Training Data ◽  
Under Sampling ◽  
Imbalanced Training Data ◽  
Binary Pso

scholarly journals An Overlapping Cell Image Synthesis Method for Imbalance Data

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Yi Ning Xie ◽  
Lian Yu ◽  
Guo Hui Guan ◽  
Yong Jun He
Keyword(s):  
Dna Ploidy ◽  
Sample Selection ◽  
Recognition Rate ◽  
Synthesis Method ◽  
Image Synthesis ◽  
Training Data ◽  
Data Set ◽  
Ploidy Analysis ◽  
Imbalance Problem ◽  
Imbalanced Training Data

DNA ploidy analysis of cells is an automation technique applied in pathological diagnosis. It is important for this technique to classify various nuclei images accurately. However, the lack of overlapping nuclei images in training data (imbalanced training data) results in low recognition rates of overlapping nuclei images. To solve this problem, a new method which synthesizes overlapping nuclei images with single-nuclei images is proposed. Firstly, sample selection is employed to make the synthesized samples representative. Secondly, random functions are used to control the rotation angles of the nucleus and the distance between the centroids of the nucleus, increasing the sample diversity. Then, the Lambert-Beer law is applied to reassign the pixels of overlapping parts, thus making the synthesized samples quite close to the real ones. Finally, all synthesized samples are added to the training sets for classifier training. The experimental results show that images synthesized by this method can solve the data set imbalance problem and improve the recognition rate of DNA ploidy analysis systems.

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