Wavelet based feature reduction method for effective classification of hyperspectral data

Feature Reduction Method for Cognition and Classification of IoT Devices Based on Artificial Intelligence

IEEE Access ◽

10.1109/access.2019.2929311 ◽

2019 ◽

Vol 7 ◽

pp. 103291-103298 ◽

Cited By ~ 1

Author(s):

Xiang Chen ◽

Xiaojun Hao

Keyword(s):

Artificial Intelligence ◽

Reduction Method ◽

Feature Reduction ◽

Iot Devices

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Automatic tumor tissue classification of oncological esophageal resectates based on hyperspectral data

10.1055/s-0039-1695116 ◽

2019 ◽

Author(s):

M Maktabi ◽

H Köhler ◽

R Thieme ◽

JP Takoh ◽

SM Rabe ◽

...

Keyword(s):

Tumor Tissue ◽

Hyperspectral Data ◽

Tissue Classification

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Classification of Imbalance Data using Tomek Link(T-Link) Combined with Random Under-sampling (RUS) as a Data Reduction Method

Journal of Informatics and Data Mining ◽

10.21767/2472-1956.100011 ◽

2016 ◽

Vol 1 (2) ◽

Cited By ~ 4

Author(s):

Elhassan T ◽

Aljurf M

Keyword(s):

Data Reduction ◽

Reduction Method ◽

Imbalance Data ◽

Under Sampling ◽

Data Reduction Method

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Hyperspectral Image Classification with Localized Graph Convolutional Filtering

Remote Sensing ◽

10.3390/rs13030526 ◽

2021 ◽

Vol 13 (3) ◽

pp. 526

Author(s):

Shengliang Pu ◽

Yuanfeng Wu ◽

Xu Sun ◽

Xiaotong Sun

Keyword(s):

Hyperspectral Image ◽

Principal Component ◽

Representation Learning ◽

Classification Performance ◽

Hyperspectral Data ◽

Spectral Graph Theory ◽

Feature Reduction ◽

Graph Representation ◽

Novel Method ◽

Local Graph

The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be how to convert hyperspectral data into irregular domains from regular grids. In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. First, we conducted principal component analysis (PCA) preprocessing to create localized hyperspectral data cubes with unsupervised feature reduction. These feature cubes combined with localized adjacent matrices were fed into the popular graph convolution network in a standard supervised learning paradigm. Finally, we succeeded in analyzing diversified land covers by considering local graph structure with graph convolutional filtering. Experiments on real hyperspectral datasets demonstrated that the presented method offers promising classification performance compared with other popular competitors.

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Using class-based feature selection for the classification of hyperspectral data

International Journal of Remote Sensing ◽

10.1080/01431161.2010.486416 ◽

2011 ◽

Vol 32 (15) ◽

pp. 4311-4326 ◽

Cited By ~ 19

Author(s):

Yasser Maghsoudi ◽

Mohammad Javad Valadan Zoej ◽

Michael Collins

Keyword(s):

Feature Selection ◽

Hyperspectral Data ◽

Selection For

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An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination

Financial Innovation ◽

10.1186/s40854-021-00243-3 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Hakan Gunduz

Keyword(s):

Reduction Method ◽

Feature Reduction ◽

The Other ◽

Recursive Feature Elimination ◽

Accuracy Rate ◽

Stock Performance ◽

Attention Model ◽

Feature Sets ◽

Accuracy Rates ◽

F Measure

AbstractIn this study, the hourly directions of eight banking stocks in Borsa Istanbul were predicted using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models. These models were trained with four different feature sets and their performances were evaluated in terms of accuracy and F-measure metrics. While the first experiments directly used the own stock features as the model inputs, the second experiments utilized reduced stock features through Variational AutoEncoders (VAE). In the last experiments, in order to grasp the effects of the other banking stocks on individual stock performance, the features belonging to other stocks were also given as inputs to our models. While combining other stock features was done for both own (named as allstock_own) and VAE-reduced (named as allstock_VAE) stock features, the expanded dimensions of the feature sets were reduced by Recursive Feature Elimination. As the highest success rate increased up to 0.685 with allstock_own and LSTM with attention model, the combination of allstock_VAE and LSTM with the attention model obtained an accuracy rate of 0.675. Although the classification results achieved with both feature types was close, allstock_VAE achieved these results using nearly 16.67% less features compared to allstock_own. When all experimental results were examined, it was found out that the models trained with allstock_own and allstock_VAE achieved higher accuracy rates than those using individual stock features. It was also concluded that the results obtained with the VAE-reduced stock features were similar to those obtained by own stock features.

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