Adaptive Data Structure Based Oversampling Algorithm for Ordinal Classification

kasiraja, Dhanalakshmi; Vijendran, Anna Saro

doi:10.11591/ijeecs.v12.i3.pp1063-1070

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…The three errors in the table represent the COEF, MAE [21], and RMSE, respectively. Dataset 5 shows the smallest figures for both MAE [23][24][25] and RMSE while dataset 3 obtains the lowest COEF figures. The MRBP models after running an MOA simulation are outlined in Table 3.…”

Section: Results and Analysismentioning

confidence: 99%

Performance evaluation of listwise deletion for impaired datasets in multiple regression-based prediction

Jittawiriyanukoon¹

2019

IJEECS

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Multiple Regression-Based Prediction (MRBP) is an emerging calculation to or analysis technique cope with the future by compiling the history of data. The MRBP characteristic will include an approximation for the associations between physical observations and predictions. MRBP is a predictive model, which will be an important source of knowledge in terms of an interesting trend to be followed in the future. However, there is impairment in the MRBP dataset, wherein each form of missing and noisy data has caused an error and is unavailable further analysis. To overcome this unavailability, so that the data analytics can be moved on, two treatment approaches are introduced. First, the given dataset is denoised; next, listwise deletion (LD) is proposed to handle the missing data. The performance of the proposed technique will be investigated by dealing with datasets that cannot be executed. Employing the Massive Online Analysis (MOA) software, the proposed model is investigated, and the results are summarized. Performance metrics, such as mean squared error (MSE), correlation coefficient (COEF), mean absolute error (MAE), root mean squared error (RMSE), and the average error percentage, are used to validate the proposed mechanism. The proposed LD projection is confirmed through actual values. The proposed LD outperforms other treatments as it only requires less state space, which reflects low computation cost, and proves its capability to overcome the limitation of analysis.

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Section: Results and Analysismentioning

confidence: 99%

Performance evaluation of listwise deletion for impaired datasets in multiple regression-based prediction

Jittawiriyanukoon¹

2019

IJEECS

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“…There are various approaches of multi-class classification problem as discussed in [34][35][36][37] such as directed acyclic graph (DAG), binary tree of SVM, one-against-all (OAA) and one-against-one (OAO). In this study, the OAO technique for multi-class classification is chosen.…”

Section: Multi-class Svm Rice Grain Classificationmentioning

confidence: 99%

Contrastive analysis of rice grain classification techniques: multi-class support vector machine vs artificial neural network

Ibrahim

Kamaruddin

Zabidi

et al. 2020

IJ-AI

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Rice is a staple food for 80% of the population in Southeast Asia. Thus, the quality control and classification of rice grain are crucial for more productive and sustainable production. This paper examines the contrastive analysis of rice grain classification performance between multi-class support vector machine (SVM) and artificial neural network (ANN). The analysis has been tested on three types of rice grain images which are Ponni, Basmati, and Brown rice. A digital image transformation analysis based on shape and color features was developed to classify the three types of rice grain. The performance of the proposed study is evaluated to 90 testing images of each rice variation. The ANN is observed to return higher classification accuracy at 93.34% using Level Sweep image transformation technique. Based on the results, it signifies that the ANN performs better classification than the multiclass SVM.

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“…There are various approaches of multi-class classification problem as discussed in [22][23][24][25] such as directed acyclic graph (DAG), binary tree of SVM, one-against-all (OAA) and one-against-one (OAO). In this study, the OAO technique for multi-class classification is chosen.…”

Section: Image Classificationmentioning

confidence: 99%

Rice Grain Classification using Multi-class Support Vector Machine (SVM)

Ibrahim

Zulkifli

Sabri

et al. 2019

IJ-AI

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Presently, the demands for rice are increasing. This will affects the need for producing and sorting rice grain in faster and exceed the normal requirement. However, the manual rice classification using naked eyes are not very accurate and only professionals are able to do it. Machine learning is found to be a suitable technique for rice classification in producing an accurate result and faster solution. Thus, a study on the classification of rice grain using an image processing technique is presented. The rice grain image went through the pre-processing process which includes the grayscale and binary conversion, and segmentation before the feature extraction process. Four attributes of shape descriptor which are area, perimeter, major axis length, and minor axis length and three attributes of color descriptor which are hue, saturation and value were extracted from each rice grain image. In another note, a Multi-class Support Vector Machine (SVM) is used to classify the three types of rice grain which are basmathi, ponni and brown rice. The performance of the proposed study is evaluated to 90 testing images which returned 92.22% of classification accuracy. The study is expected to assist the Agrotechnology industry in automatic classification of rice grain in the future.

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Adaptive Data Structure Based Oversampling Algorithm for Ordinal Classification

Cited by 3 publications

References 9 publications

Performance evaluation of listwise deletion for impaired datasets in multiple regression-based prediction

Performance evaluation of listwise deletion for impaired datasets in multiple regression-based prediction

Contrastive analysis of rice grain classification techniques: multi-class support vector machine vs artificial neural network

Rice Grain Classification using Multi-class Support Vector Machine (SVM)

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