scholarly journals Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy

2014 ◽  
Author(s):  
Justin Martineau ◽  
Lu Chen ◽  
Doreen Cheng ◽  
Amit Sheth
Keyword(s):  
Active Learning ◽  
Data Quality ◽  
Classification Accuracy ◽  
Feature Weighting ◽  
Weighting Methods

scholarly journals Sentiment Classification of Financial News Using Statistical Features

2017 ◽  
Vol 31 (03) ◽  
pp. 1750006 ◽  
Author(s):  
Sepideh Foroozan Yazdani ◽  
Masrah Azrifah Azmi Murad ◽  
Nurfadhlina Mohd Sharef ◽  
Yashwant Prasad Singh ◽  
Ahmed Razman Abdul Latiff
Keyword(s):  
Classification Accuracy ◽  
Feature Weighting ◽  
Sentiment Classification ◽  
News Article ◽  
Term Frequency ◽  
Financial News ◽  
Document Frequency ◽  
Feature Spaces ◽  
Weighting Methods

Sentiment classification of financial news deals with the identification of positive and negative news so that they can be applied in decision support systems for stock trend predictions. This paper explores several types of feature spaces as different data spaces for sentiment classification of the news article. Experiments are conducted using [Formula: see text]-gram models unigram, bigram and the combination of unigram and bigram as feature extraction with traditional feature weighting methods (binary, term frequency (TF), and term frequency-document frequency (TF-IDF)), while document frequency (DF) was used in order to generate feature spaces with different dimensions to evaluate [Formula: see text]-gram models and traditional feature weighting methods. We performed some experiments to measure the classification accuracy of support vector machine (SVM) with two kernel methods of Linear and Gaussian radial basis function (RBF). We concluded that feature selection and feature weighting methods can have a substantial role in sentiment classification. Furthermore, the results showed that the proposed work which combined unigram and bigram along with TF-IDF feature weighting method and optimized RBF kernel SVM produced high classification accuracy in financial news classification.

scholarly journals Active Learning Algorithms for the Classification of Hyperspectral Sea Ice Images

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yanling Han ◽  
Jing Ren ◽  
Zhonghua Hong ◽  
Yun Zhang ◽  
Long Zhang ◽  
...  
Keyword(s):  
Active Learning ◽  
Sea Ice ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Baffin Bay ◽  
Training Samples ◽  
The Difference ◽  
Marine Disasters ◽  
Ice Detection

Sea ice is one of the most critical marine disasters, especially in the polar and high latitude regions. Hyperspectral image is suitable for monitoring the sea ice, which contains continuous spectrum information and has better ability of target recognition. The principal bottleneck for the classification of hyperspectral image is a large number of labeled training samples required. However, the collection of labeled samples is time consuming and costly. In order to solve this problem, we apply the active learning (AL) algorithm to hyperspectral sea ice detection which can select the most informative samples. Moreover, we propose a novel investigated AL algorithm based on the evaluation of two criteria: uncertainty and diversity. The uncertainty criterion is based on the difference between the probabilities of the two classes having the highest estimated probabilities, while the diversity criterion is based on a kernelk-means clustering technology. In the experiments of Baffin Bay in northwest Greenland on April 12, 2014, our proposed AL algorithm achieves the highest classification accuracy of 89.327% compared with other AL algorithms and random sampling, while achieving the same classification accuracy, the proposed AL algorithm needs less labeling cost.

scholarly journals Twin-Systems to Explain Artificial Neural Networks using Case-Based Reasoning: Comparative Tests of Feature-Weighting Methods in ANN-CBR Twins for XAI

2019 ◽  
Author(s):  
Eoin M. Kenny ◽  
Mark T. Keane
Keyword(s):  
Neural Networks ◽  
Evaluation Method ◽  
Nearest Neighbor ◽  
Image Data ◽  
Black Box ◽  
Feature Weighting ◽  
Case Based Reasoning ◽  
Artificial Neural ◽  
Weighting Methods ◽  
Case Based

In this paper, twin-systems are described to address the eXplainable artificial intelligence (XAI) problem, where a black box model is mapped to a white box “twin” that is more interpretable, with both systems using the same dataset. The framework is instantiated by twinning an artificial neural network (ANN; black box) with a case-based reasoning system (CBR; white box), and mapping the feature weights from the former to the latter to find cases that explain the ANN’s outputs. Using a novel evaluation method, the effectiveness of this twin-system approach is demonstrated by showing that nearest neighbor cases can be found to match the ANN predictions for benchmark datasets. Several feature-weighting methods are competitively tested in two experiments, including our novel, contributions-based method (called COLE) that is found to perform best. The tests consider the ”twinning” of traditional multilayer perceptron (MLP) networks and convolutional neural networks (CNN) with CBR systems. For the CNNs trained on image data, qualitative evidence shows that cases provide plausible explanations for the CNN’s classifications.

scholarly journals A Three-Hierarchy Evaluation of Polarimetric Performance of GF-3, Compared with ALOS-2/PALSAR-2 and RADARSAT-2

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1493
Author(s):  
Zezhong Wang ◽  
Jian Jiao ◽  
Qiming Zeng ◽  
Junyi Liu
Keyword(s):  
Image Quality ◽  
Synthetic Aperture Radar ◽  
Data Quality ◽  
Classification Accuracy ◽  
Synthetic Aperture ◽  
Polarimetric Sar ◽  
Geophysical Research ◽  
Validation Data ◽  
Operational Space

GaoFen-3 (GF-3) is the first Chinese civilian multi-polarization synthetic aperture radar (SAR) satellite, launched on 10 August of 2016, and put into operation at the end of January 2017. The polarimetric SAR (PolSAR) system of GF-3 is able to provide quad-polarization (quad-pol) images in a variety of geophysical research and applications. However, this ability increases the complexity of maintaining image quality and calibration. As a result, to evaluate the quality of polarimetric data, polarimetric signatures are necessary to guarantee accuracy. Compared with some other operational space-borne PolSAR systems, such as ALOS-2/PALSAR-2 (ALOS-2) and RADARSAT-2, GF-3 has less reported calibration and image quality files, forcing users to validate the quality of polarimetric imagery of GF-3 before quantitative applications. In this study, without the validation data obtained from a calibration infrastructure, an innovative, three-hierarchy strategy was proposed to assess PolSAR data quality, in which the performance of GF-3 data was evaluated with ALOS-2 and RADARSAT-2 data as references. Experimental results suggested that: (1) PolSAR data of GF-3 satisfied backscatter reciprocity, similar with that of RADARSAT-2; (2) most of the GF-3 PolSAR images had no signs of polarimetric distortion affecting decomposition, and the system of GF-3 may have been improved around May 2017; and (3) the classification accuracy of GF-3 varied from 75.0% to 91.4% because of changing image-acquiring situations. In conclusion, the proposed three-hierarchy approach has the ability to evaluate polarimetric performance. It proved that the residual polarimetric distortion of calibrated GF-3 PolSAR data remained at an insignificant level, with reference to that of ALOS-2 and RADARSAT-2, and imposed no significant impact on the polarimetric decomposition components and classification accuracy.

Sign in / Sign up

Export Citation Format

Share Document