A new image classification method based on modified condensed nearest neighbor and convolutional neural networks

Liang, Tu; Xiaoli, Xu; Xiao, Pengcheng

doi:10.1016/j.patrec.2017.05.019

Cited by 37 publications

(19 citation statements)

References 18 publications

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“…Synthetic Minority Over-sampling (SMOTE) is also a common technique that applies an interpolation between several minority class samples in order to create new artificial minority class samples [73]. Condensed Nearest Neighbor Rule (CNNR) is an algorithm that aims to reclassify samples from the majority class to the minority class using a 1-nearest neighbor classification technique, and it eliminates the distant samples because they are irrelevant for the learning process [74].…”

Section: Data Balancing Methodsmentioning

confidence: 99%

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

et al. 2020

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Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use.

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Section: Data Balancing Methodsmentioning

confidence: 99%

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

et al. 2020

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“…For example, Li et al [10] proposed a hybrid feature selection strategy based on support vector machine (SVM) and genetic algorithm (GA), and classified hyperspectral remote sensing images by the strategy. Liang et al [11] put forward an image classification method based on convolutional neural network (CNN). Han et al [12] presented a multi-label image classification algorithm based on neural network (NN).…”

Section: Literature Reviewmentioning

confidence: 99%

A Fast Recognition Algorithm of Online Social Network Images Based on Deep Learning

Meng¹,

Mao²,

Zhang³

et al. 2019

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In recent years, a massive number of images have generated on the online social network (OSN). This calls for an efficient and rapid way to extract the information from the OSN images. This paper puts forward an OSN image classification method based on improved deep belief network (DBN) and support vector machine (SVM). In the proposed method, the image classification is enhanced by improving the self-adaptive learning rate based on incremental discrimination of reconstruction error and the weight update criteria with increasing momentum. The effectiveness of our method was confirmed through an image recognition experiment on OSN images obtained from Sina Weibo public platform, in comparison with four commonly used classification methods. The research results shed new light on feature extraction and classification of OSN images.

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“…The common classification algorithms are mainly based on statistical method [3,4], decision tree (DT) [5][6][7], and the neural network (NN) [8,9]. In addition, some classification algorithms draw the merits from the k-nearest neighbor (k-NN) algorithm [10,11], support vector machine (SVM) [12] and so on.…”

Section: Introductionmentioning

confidence: 99%

“…This paper mainly improves the backpropagation (BP) algorithm for the classification of big data samples. Firstly, the input data were normalized into the range of [0,10], such that all input samples belong to the same order of magnitude. Then, the traditional BP algorithm was improved based on the backpropagation neural network (BPNN), a typical multilayer perceptron (MLP), the least mean square (LMS) algorithm, and a self-designed momentum factor.…”

Section: Introductionmentioning

confidence: 99%

A Novel Big Data Classification Algorithm Based on Backpropagation Neural Network

Wei-xiang¹

2020

RIA

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Most of the existing classification algorithms perform poorly facing big data samples. To solve the problem, this paper puts forward a novel classification algorithm based on backpropagation neural network (BPNN). Firstly, the original data were normalized to the same order of magnitude. The normalization improves the consistency of the input data, facilitating the classification. Next, the least mean square (LMS) algorithm and the BPNN were integrated into a novel batch learning BP algorithm. Finally, several experiments were carried out, revealing that our algorithm outshined the traditional BP algorithm in the classification accuracy. This research provides a good reference for the accurate classification of big data samples.

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A new image classification method based on modified condensed nearest neighbor and convolutional neural networks

Cited by 37 publications

References 18 publications

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

A Fast Recognition Algorithm of Online Social Network Images Based on Deep Learning

A Novel Big Data Classification Algorithm Based on Backpropagation Neural Network

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