scholarly journals A Novel Framework Using Deep Auto-Encoders Based Linear Model for Data Classification

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6378
Author(s):  
Ahmad M. Karim ◽  
Hilal Kaya ◽  
Mehmet Serdar Güzel ◽  
Mehmet R. Tolun ◽  
Fatih V. Çelebi ◽  
...  
Keyword(s):  
Linear Model ◽  
Processing System ◽  
Data Classification ◽  
Pso Algorithm ◽  
Classification Problem ◽  
Classification Performance ◽  
Softmax Classifier ◽  
Public Datasets ◽  
High Level ◽  
Hidden Neurons

This paper proposes a novel data classification framework, combining sparse auto-encoders (SAEs) and a post-processing system consisting of a linear system model relying on Particle Swarm Optimization (PSO) algorithm. All the sensitive and high-level features are extracted by using the first auto-encoder which is wired to the second auto-encoder, followed by a Softmax function layer to classify the extracted features obtained from the second layer. The two auto-encoders and the Softmax classifier are stacked in order to be trained in a supervised approach using the well-known backpropagation algorithm to enhance the performance of the neural network. Afterwards, the linear model transforms the calculated output of the deep stacked sparse auto-encoder to a value close to the anticipated output. This simple transformation increases the overall data classification performance of the stacked sparse auto-encoder architecture. The PSO algorithm allows the estimation of the parameters of the linear model in a metaheuristic policy. The proposed framework is validated by using three public datasets, which present promising results when compared with the current literature. Furthermore, the framework can be applied to any data classification problem by considering minor updates such as altering some parameters including input features, hidden neurons and output classes.

scholarly journals A Novel Model for Imbalanced Data Classification

2020 ◽  
Vol 34 (04) ◽  
pp. 6680-6687
Author(s):  
Jian Yin ◽  
Chunjing Gan ◽  
Kaiqi Zhao ◽  
Xuan Lin ◽  
Zhe Quan ◽  
...  
Keyword(s):  
Imbalanced Data ◽  
Data Classification ◽  
Classification Performance ◽  
Classification Model ◽  
Proposed Model ◽  
Imbalanced Data Classification ◽  
Public Datasets ◽  
Distribution Cost ◽  
Novel Model ◽  
Learning Data

Recently, imbalanced data classification has received much attention due to its wide applications. In the literature, existing researches have attempted to improve the classification performance by considering various factors such as the imbalanced distribution, cost-sensitive learning, data space improvement, and ensemble learning. Nevertheless, most of the existing methods focus on only part of these main aspects/factors. In this work, we propose a novel imbalanced data classification model that considers all these main aspects. To evaluate the performance of our proposed model, we have conducted experiments based on 14 public datasets. The results show that our model outperforms the state-of-the-art methods in terms of recall, G-mean, F-measure and AUC.

scholarly journals Deep Learning for Hyperspectral Data Classification through Exponential Momentum Deep Convolution Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Qi Yue ◽  
Caiwen Ma
Keyword(s):  
Data Classification ◽  
Classification Problem ◽  
Hyperspectral Data ◽  
Support Vector ◽  
New Classification ◽  
Spatial Features ◽  
Classification Framework ◽  
Deep Convolution Neural Network ◽  
Quantitative Validation ◽  
High Level

Classification is a hot topic in hyperspectral remote sensing community. In the last decades, numerous efforts have been concentrated on the classification problem. Most of the existing studies and research efforts are following the conventional pattern recognition paradigm, which is based on complex handcrafted features. However, it is rarely known which features are important for the problem. In this paper, a new classification skeleton based on deep machine learning is proposed for hyperspectral data. The proposed classification framework, which is composed of exponential momentum deep convolution neural network and support vector machine (SVM), can hierarchically construct high-level spectral-spatial features in an automated way. Experimental results and quantitative validation on widely used datasets showcase the potential of the developed approach for accurate hyperspectral data classification.

CREDIT SCORING USING MULTI-KERNEL SUPPORT VECTOR MACHINE AND CHAOS PARTICLE SWARM OPTIMIZATION

2012 ◽  
Vol 11 (03) ◽  
pp. 1250019 ◽  
Author(s):  
YUN LING ◽  
QIUYAN CAO ◽  
HUA ZHANG
Keyword(s):  
Particle Swarm Optimization ◽  
Kernel Function ◽  
Credit Scoring ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Classification Problem ◽  
Classification Performance ◽  
Kernel Functions ◽  
Support Vector ◽  
Swarm Optimization

Consumer credit scoring is considered as a crucial issue in the credit industry. SVM has been successfully utilized for classification in many areas including credit scoring. Kernel function is vital when applying SVM to classification problem for enhancing the prediction performance. Currently, most of kernel functions used in SVM are single kernel functions such as the radial basis function (RBF) which has been widely used. On the basis of the existing kernel functions, this paper proposes a multi-kernel function to improve the learning and generalization ability of SVM by integrating several single kernel functions. Chaos particle swarm optimization (CPSO) which is a kind of improved PSO algorithm is utilized to optimize parameters and to select features simultaneously. Two UCI credit data sets are used as the experimental data to evaluate the classification performance of the proposed method.

scholarly journals A Network-Based High-Level Data Classification Algorithm Using Betweenness Centrality

2020 ◽  
Author(s):  
Esteban Zuñiga ◽  
Liang Zhao
Keyword(s):  
Betweenness Centrality ◽  
Input Data ◽  
Data Classification ◽  
Classification Performance ◽  
Classification Techniques ◽  
Level Data ◽  
Physical Attributes ◽  
High Level ◽  
Physical Features ◽  
Pure Network

Data classification is a major machine learning paradigm, which has been widely applied to solve a large number of real-world problems. Traditional data classification techniques consider only physical features (e.g., distance, similarity, or distribution) of the input data. For this reason, those are called low-level classification. On the other hand, the human (animal) brain performs both low and high orders of learning, and it has a facility in identifying pat-terns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is referred to as high-level classification. Several high-level classification techniques have been developed, which make use of complex networks to characterize data patterns and have obtained promising results. In this paper, we propose a pure network-based high-level classification technique that uses the betweenness centrality measure. We test this model in nine different real datasets and compare it with other nine traditional and well-known classification models. The results show us a competent classification performance.

scholarly journals Compound Multiscale Weak Dense Network with Hybrid Attention for Hyperspectral Image Classification

2021 ◽  
Vol 13 (16) ◽  
pp. 3305
Author(s):  
Zixian Ge ◽  
Guo Cao ◽  
Hao Shi ◽  
Youqiang Zhang ◽  
Xuesong Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Hyperspectral Image ◽  
Spectral Feature ◽  
Research Direction ◽  
Classification Performance ◽  
Performance Comparison ◽  
Attention Mechanism ◽  
Semantic Features ◽  
Public Datasets ◽  
High Level

Recently, hyperspectral image (HSI) classification has become a popular research direction in remote sensing. The emergence of convolutional neural networks (CNNs) has greatly promoted the development of this field and demonstrated excellent classification performance. However, due to the particularity of HSIs, redundant information and limited samples pose huge challenges for extracting strong discriminative features. In addition, addressing how to fully mine the internal correlation of the data or features based on the existing model is also crucial in improving classification performance. To overcome the above limitations, this work presents a strong feature extraction neural network with an attention mechanism. Firstly, the original HSI is weighted by means of the hybrid spectral–spatial attention mechanism. Then, the data are input into a spectral feature extraction branch and a spatial feature extraction branch, composed of multiscale feature extraction modules and weak dense feature extraction modules, to extract high-level semantic features. These two features are compressed and fused using the global average pooling and concat approaches. Finally, the classification results are obtained by using two fully connected layers and one Softmax layer. A performance comparison shows the enhanced classification performance of the proposed model compared to the current state of the art on three public datasets.

scholarly journals NHL Pathological Image Classification Based on Hierarchical Local Information and GoogLeNet-Based Representations

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Jie Bai ◽  
Huiyan Jiang ◽  
Siqi Li ◽  
Xiaoqi Ma
Keyword(s):  
Characteristic Curve ◽  
Classification Performance ◽  
Lymphocytic Leukemia ◽  
Classification Model ◽  
Softmax Classifier ◽  
Proposed Model ◽  
Mantle Cell ◽  
Intrinsic Complexity ◽  
Hematoxylin And Eosin ◽  
High Level

Background. Accurate classification for different non-Hodgkin lymphomas (NHL) is one of the main challenges in clinical pathological diagnosis due to its intrinsic complexity. Therefore, this paper proposes an effective classification model for three types of NHL pathological images, including mantle cell lymphoma (MCL), follicular lymphoma (FL), and chronic lymphocytic leukemia (CLL). Methods. There are three main parts with respect to our model. First, NHL pathological images stained by hematoxylin and eosin (H&E) are transferred into blue ratio (BR) and Lab spaces, respectively. Then specific patch-level textural and statistical features are extracted from BR images and color features are obtained from Lab images both using a hierarchical way, yielding a set of hand-crafted representations corresponding to different image spaces. A random forest classifier is subsequently trained for patch-level classification. Second, H&E images are cropped and fed into a pretrained google inception net (GoogLeNet) for learning high-level representations and a softmax classifier is used for patch-level classification. Finally, three image-level classification strategies based on patch-level results are discussed including a novel method for calculating the weighted sum of patch results. Different classification results are fused at both feature 1 and image levels to obtain a more satisfactory result. Results. The proposed model is evaluated on a public IICBU Malignant Lymphoma Dataset and achieves an improved overall accuracy of 0.991 and area under the receiver operating characteristic curve of 0.998. Conclusion. The experimentations demonstrate the significantly increased classification performance of the proposed model, indicating that it is a suitable classification approach for NHL pathological images.

scholarly journals Signal Classification Algorithms over Time Selective Channels

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1714
Author(s):  
Mohamed Marey ◽  
Hala Mostafa
Keyword(s):  
Block Code ◽  
Classification Problem ◽  
Classification Performance ◽  
Signal Classification ◽  
Final Decision ◽  
Time Block ◽  
Channel Response ◽  
Over Time ◽  
Parallel Fashion

In this work, we propose a general framework to design a signal classification algorithm over time selective channels for wireless communications applications. We derive an upper bound on the maximum number of observation samples over which the channel response is an essential invariant. The proposed framework relies on dividing the received signal into blocks, and each of them has a length less than the mentioned bound. Then, these blocks are fed into a number of classifiers in a parallel fashion. A final decision is made through a well-designed combiner and detector. As a case study, we employ the proposed framework on a space-time block-code classification problem by developing two combiners and detectors. Monte Carlo simulations show that the proposed framework is capable of achieving excellent classification performance over time selective channels compared to the conventional algorithms.

Lung nodule classification using combination of CNN, second and higher order texture features

2021 ◽  
pp. 1-9
Author(s):  
Amrita Naik ◽  
Damodar Reddy Edla
Keyword(s):  
Malignant Tumors ◽  
Early Stage ◽  
Lung Nodule ◽  
Texture Features ◽  
Svm Classifier ◽  
Softmax Classifier ◽  
Spatial Features ◽  
High Level ◽  
Learning Architectures ◽  
Nodule Classification

Lung cancer is the most common cancer throughout the world and identification of malignant tumors at an early stage is needed for diagnosis and treatment of patient thus avoiding the progression to a later stage. In recent times, deep learning architectures such as CNN have shown promising results in effectively identifying malignant tumors in CT scans. In this paper, we combine the CNN features with texture features such as Haralick and Gray level run length matrix features to gather benefits of high level and spatial features extracted from the lung nodules to improve the accuracy of classification. These features are further classified using SVM classifier instead of softmax classifier in order to reduce the overfitting problem. Our model was validated on LUNA dataset and achieved an accuracy of 93.53%, sensitivity of 86.62%, the specificity of 96.55%, and positive predictive value of 94.02%.

scholarly journals Improved YOLO Network for Free-Angle Remote Sensing Target Detection

2021 ◽  
Vol 13 (11) ◽  
pp. 2171
Author(s):  
Yuhao Qing ◽  
Wenyi Liu ◽  
Liuyan Feng ◽  
Wanjia Gao
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Target Detection ◽  
Multiple Scales ◽  
Classification Problem ◽  
Input Image ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Regression Problem ◽  
Public Datasets

Despite significant progress in object detection tasks, remote sensing image target detection is still challenging owing to complex backgrounds, large differences in target sizes, and uneven distribution of rotating objects. In this study, we consider model accuracy, inference speed, and detection of objects at any angle. We also propose a RepVGG-YOLO network using an improved RepVGG model as the backbone feature extraction network, which performs the initial feature extraction from the input image and considers network training accuracy and inference speed. We use an improved feature pyramid network (FPN) and path aggregation network (PANet) to reprocess feature output by the backbone network. The FPN and PANet module integrates feature maps of different layers, combines context information on multiple scales, accumulates multiple features, and strengthens feature information extraction. Finally, to maximize the detection accuracy of objects of all sizes, we use four target detection scales at the network output to enhance feature extraction from small remote sensing target pixels. To solve the angle problem of any object, we improved the loss function for classification using circular smooth label technology, turning the angle regression problem into a classification problem, and increasing the detection accuracy of objects at any angle. We conducted experiments on two public datasets, DOTA and HRSC2016. Our results show the proposed method performs better than previous methods.

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