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Classification accuracy
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Part 1 of ebook "An introduction to statistical learning with applications in R" provides readers with contents including: Chapter 1 - Introduction; Chapter 2 - Statistical learning; Chapter 3 - Linear regression; Chapter 4 - Classification; Chapter 5 - Resampling methods;...
212p
daonhiennhien
03-07-2024
1
1
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Part 1 of ebook "Principles of data mining (Third edition)" provides readers with contents including: Chapter 1 - Introduction to data mining; Chapter 2 - Data for data mining; Chapter 3 - Introduction to classification, Naïve bayes and nearest neighbour; Chapter 4 - Using decision trees for classification; Chapter 5 - Decision tree induction, using entropy for attribute selection; Chapter 6 - Decision tree induction, using frequency tables for attribute selection; Chapter 7 - Estimating the predictive accuracy of a classifier;...
263p
daonhiennhien
03-07-2024
1
1
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This study proposes a threshold method in the preprocessing stage to improve human activity classification accuracy by determining the region of meaningful information (RMI) on the spectrogram. Initially, a mask function, which is created by a certain threshold value, is applied to the input spectrogram to highlight the RMI from the micro-Doppler (m-D) signatures.
9p
visergeyne
18-06-2024
0
0
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The research "Application of random forest algorithm and google colab for land cover classification" is to provide the approach RF with Google Colab environment for classifying land cover in Quang Binh province, Vietnam using sentinel - 2 image in August 2021. The results have indicated the ability of this research direction with an overall accuracy above 80 %.
9p
tukhauquantuong1011
22-04-2024
6
2
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The paper deals with the task of assessing the financial condition of enterprises. To solve it, we prove the necessity of building a neural network model for supply chain. A set of financial ratios is defined as the input parameters of the model: the current liquidity ratio of the enterprise, the equity ratio, the equity turnover ratio, and the return on equity ratio. The output parameters were the types of the financial condition of enterprises: an unstable state (regression), a normal state (stable) and an absolutely stable state (progression).
6p
longtimenosee09
08-04-2024
2
1
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Exome sequencing is now mainstream in clinical practice. However, identification of pathogenic Mendelian variants remains time-consuming, in part, because the limited accuracy of current computational prediction methods requires manual classification by experts.
11p
vibransone
28-03-2024
3
2
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This study aims to optimize the classification process of providing assistance to Indonesian Telematics Small and Medium Enterprises (SMEs) using a deep learning approach. The data used is the 2016 Economic Census data. The research was conducted comprehensively through the process of comparing performance through several approaches. Deep learning performance shows an optimal accuracy rate of 99.03%, higher than other approaches of the Adaboost and Adaboos-Bagging Ensemble (92.0%), LVQ (93.11%) and Backpropagation (89.1%).
7p
longtimenosee04
06-03-2024
5
1
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In this research "Classification and regression tree model to predict the probability of a backorder in uncertain supply chain", we develop Classification and Regression Tree (CART) model that uses previously known parameters to predict the likelihood of a product being backordered. We also use different model parameters to evaluate the accuracy of the model. Result shows that the developed model can help decision makers to identify the key factors that lead to a product backordering.
8p
longtimenosee04
06-03-2024
3
1
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This study proposes a method to enhance the accuracy of the MobileNetV3 deep learning model. The technique involves removing duplicate and outlier images to improve the efficiency of the MobileNetV3 model using K-Means.
8p
vigrab
02-02-2024
2
1
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Minerals, with their intricate chemical compositions and crystalline structures, play a pivotal role in diverse chemical processes, applications, and research. Traditionally, their classification was achieved through observational and chemical techniques. However, with increasing sample sizes, these methods often proved time-consuming. Recent advances in Artificial Intelligence (AI) and Deep Learning (DL) promise transformative improvements in the speed and accuracy of mineral classification.
14p
vigojek
02-02-2024
4
1
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This paper proposes two hybrid ensemble machine learning approaches that integrate random subspace ensemble with bagging and boosting to enhance classification performance with high-dimensional data. Experimental results demonstrate that these methods significantly improve classification accuracy with highdimensional data.
14p
viengels
25-08-2023
6
4
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In multi-label text classification, each textual document is assigned 1 or more labels. As an important task that has broad applications in biomedicine, a number of different computational methods have been proposed. Many of these methods, however, have only modest accuracy or efficiency and limited success in practical use.
7p
visteverogers
24-06-2023
3
2
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In this paper, we propose using PCA for dimension reduction and then using decesion tree algorithms to search the space of eigenvectors with the goal of selecting a subset of eigenvectors encoding important information.
4p
vifalcon
18-05-2023
7
2
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The paper proposed an ensemble classifier (EC) that is combined by parallel single classifiers. The single classifiers can compensate for the others by combining in parallel. Then, the EC can improve classification accuracy. The paper proposed the use of Multi-layer Perceptron Networks (MLPN) to build EC. The study is tested on IEEE 39-bus power system network.
6p
vidoctorstrange
06-05-2023
7
3
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In this paper, we build an incremental algorithm to find the approximate reduct according to the combined filter-wrapper approach. Experimental results on a number of sample datasets show that the proposed incremental algorithm is more efficient than some other incremental algorithms following the filter approach in terms of the number of reductive set attributes and classification accuracy.
8p
vineville
08-02-2023
6
2
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This paper proposes an integration of one-againstone (OAO) strategy and support vector machines (SVM) to diagnose multiple faults of steel plates. The OAO is adopted to address multi-classification tasks in the binary SVM (i.e, OAOSVMs). The performance of the proposed model is compared with that of optimization algorithm-based SVM.
4p
vispyker
16-11-2022
6
1
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In this article, the author proposes that Landsat images should be pre-processed using the DOS method for atmospheric correction and the Gram-Schmidt method for image enhancement. According to the results, the overall classification accuracy of multi-temporal images achieved over 85%, and Landsat images are suitable for classifying land cover on small islands in Truong Sa Islands, such as Subi Reef.
10p
vimclaren
20-10-2022
10
2
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This paper investigates convolutional neural networks (CNN) to classify 26 types of signal modulation under the influence of five different fading channels and Gausian noise with SNR from -20 dB to +18 dB. Specifically, five CNN models, including ResNet18, SqueezeNet, GoogleNet, MobileNet, and RepVGG, are taken into account for a accuracy competition to discover the best one.
8p
vigeneralmotors
13-07-2022
1084
9
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In this paper, we propose a new Improvement Least Square - Support Vector Machine (called ILS-SVM) for binary classification problems with a class-vs-cluster strategy. Experimental results show that the ILS-SVM training time is faster than that of TSVM, and the ILS-SVM accuracy is better than LSTSVM and TSVM in most cases.
6p
viplato
05-04-2022
17
1
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Colonoscopy image classification is an image classification task that predicts whether colonoscopy images contain polyps or not. It is an important task input for an automatic polyp detection system. Recently, deep neural networks have been widely used for colonoscopy image classification due to the automatic feature extraction with high accuracy.
11p
vikissinger
03-03-2022
11
1
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