Editing training data for multi-label classification with the k-nearest neighbor rule

Kanj, Sawsan; Abdallah, Fahed; Denœux, Thierry; Tout, Kifah

doi:10.1007/s10044-015-0452-8

Cited by 54 publications

(22 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The Wilcoxon paired signed-rank test was used only when two models were compared [60][61][62]. The Friedman test was used for the multiple model comparisons [63][64][65] since multiple The first and second rows show the rate of occurrence of line and polygon graphs as density using the reference data. An area with a high occurrence rate means that the majority of graphs were plotted over the area.…”

Section: Lake Tappsmentioning

confidence: 99%

Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery

et al. 2020

View full text Add to dashboard Cite

This study compares some different types of spectral domain transformations for convolutional neural network (CNN)-based land cover classification. A novel approach was proposed, which transforms one-dimensional (1-D) spectral vectors into two-dimensional (2-D) features: Polygon graph images (CNN-Polygon) and 2-D matrices (CNN-Matrix). The motivations of this study are that (1) the shape of the converted 2-D images is more intuitive for human eyes to interpret when compared to 1-D spectral input; and (2) CNNs are highly specialized and may be able to similarly utilize this information for land cover classification. Four seasonal Landsat 8 images over three study areas—Lake Tapps, Washington, Concord, New Hampshire, USA, and Gwangju, Korea—were used to evaluate the proposed approach for nine land cover classes compared to several other methods: Random forest (RF), support vector machine (SVM), 1-D CNN, and patch-based CNN. Oversampling and undersampling approaches were conducted to examine the effect of the sample size on the model performance. The CNN-Polygon had better performance than the other methods, with overall accuracies of about 93%–95 % for both Concord and Lake Tapps and 80%–84% for Gwangju. The CNN-Polygon particularly performed well when the training sample size was small, less than 200 per class, while the CNN-Matrix resulted in similar or higher performance as sample sizes became larger. The contributing input variables to the models were carefully analyzed through sensitivity analysis based on occlusion maps and accuracy decreases. Our result showed that a more visually intuitive representation of input features for CNN-based classification models yielded higher performance, especially when the training sample size was small. This implies that the proposed graph-based CNNs would be useful for land cover classification where reference data are limited.

show abstract

Section: Lake Tappsmentioning

confidence: 99%

Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Currently there have been a number of mature classification algorithms, such as K-Nearest Neighbor [5], Support Vector Machine [6], Decision Tree [7], Artificial Neural Network [8] and so on. These classification algorithms have been successfully applied in people's life, production, transportation and other fields.…”

Section: Related Workmentioning

confidence: 99%

Big Data Mining Optimization Algorithm Based on Machine Learning Model

Jiao¹

2020

RIA

View full text Add to dashboard Cite

In a big data mining optimization algorithm, the classification algorithm plays an important role. At present, there are many popular classification algorithms based on machine learning. Aiming at the problems of existing big data classification algorithms, two improved strategies and implementation methods are proposed in this paper. First of all, before the data training, the orders of magnitude of the original data are normalized to achieve better data pre-processing and classification. Then with the least mean square algorithm and the BP neural network classification algorithm as the basic algorithms, an improved batch learning BP algorithm is designed, based on the rules of batch learning. The experimental results indicate that the improved batch learning BP algorithm can better solve the imbalanced classification problem in big data.

show abstract

“…Regarding relabelling procedures, many research have been carried out to identify suspect examples with the intention to suppress or relabel them into a concurrent more appropriate class [15,20]. This is generally done to enhance the performance.…”

Section: Related Workmentioning

confidence: 99%

Evidential Classification of Incomplete Data via Imprecise Relabelling: Application to Plastic Sorting

Jacquin

Imoussaten

Trousset

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Besides the ecological issues, recycling of plastics involves economical matters that encourage industrial firms to invest in the field. Part of them have focused on the waste sorting phase by designing optical device able to discriminate on line among plastics categories. For achieving ecological and economical objectives, sorting errors must be minimized to avoid serious recycling problems and significant quality degradation of the final recycled product. Even with the most recent acquisition technologies based on spectra imaging, plastic recognition remains a tough task due to the presence of imprecision and uncertainty, e.g., variability in the measurement due to atmospheric disturbances, ageing of plastics, dark or black coloured materials etc. The enhancement of the recent sorting techniques based on classification algorithms leads to rather good performance results, however for such applications, the remaining errors have serious consequences. In this article, we propose an imprecise classification algorithm to minimize sorting errors of standard classifiers when dealing with incomplete data by both integrating the processing of classification's doubt and hesitation in the decision process and improving the classification performances. To this aim, we propose a relabelling procedure that allows to better represent the imprecision of the learning data and we introduce the belief functions framework to represent the posterior probability provided by a classifier. Finally, the performances of our approach compared to existing imprecise classifiers is illustrated on the sorting problem of four plastics categories from mid-wavelength infra-red spectra acquired in an industrial context.

show abstract

Editing training data for multi-label classification with the k-nearest neighbor rule

Cited by 54 publications

References 43 publications

Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery

Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery

Big Data Mining Optimization Algorithm Based on Machine Learning Model

Evidential Classification of Incomplete Data via Imprecise Relabelling: Application to Plastic Sorting

Contact Info

Product

Resources

About