An imprecise extension of SVM-based machine learning models

Utkin, Lev V.

doi:10.1016/j.neucom.2018.11.053

Cited by 58 publications

(26 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reason may be that RF has advantages over SVM when dealing with unbalanced data. The repeated random sub-sampling in RF has been found to be very effective in dealing with an imbalanced dataset [54] whereas SVM assumes that the class distribution in the dataset is uniform [55].…”

Section: Results Of Experiments Imentioning

confidence: 99%

Forecasting Agricultural Commodity Prices Using Model Selection Framework With Time Series Features and Forecast Horizons

et al. 2020

View full text Add to dashboard Cite

The fluctuations of agricultural commodity prices have a great impact on people's daily lives as well as the inputs and outputs of agricultural production. An accurate forecast of commodity prices is therefore essential if agricultural authorities are to make scientific decisions. To forecast prices more adaptively, this study proposes a novel model selection framework which includes time series features and forecast horizons. Twenty-nine features are used to depict agricultural commodity prices and three intelligent models are specified as the candidate forecast models; namely, artificial neural network (ANN), support vector regression (SVR), and extreme learning machine (ELM). Both random forest (RF) and support vector machine (SVM) are applied to learn the underlying relationships between the features and the performances of the candidate models. Additionally, a minimum redundancy and maximum relevance approach (MRMR) is employed to reduce feature redundancy and further improve the forecast accuracy. The experimental results demonstrate that, firstly, the proposed model selection framework has a better forecast performance compared with the optimal candidate model and simple model average; secondly, feature reduction is a workable approach to further improve the performance of the model selection framework; and thirdly, for bean and pig grain products, different distributions of the time series features lead to a different selection of the optimal models. INDEX TERMS Model selection, agricultural commodity, price forecasting, time series features, forecast horizons.

show abstract

Section: Results Of Experiments Imentioning

confidence: 99%

Forecasting Agricultural Commodity Prices Using Model Selection Framework With Time Series Features and Forecast Horizons

et al. 2020

View full text Add to dashboard Cite

show abstract

“…On the other hand the Random Forest is enhanced using the back propagation approach to remove the unwanted tress to improve the final decision. The performance of the present study is compared with top classification algorithms: support Vector Machine (SVM) [18],k-Nearest Neighbors (KNN) [12], Naïve bayes(NB) [11], Artificial neural network (ANN) [19]. A.…”

Section: Proposed Methodsmentioning

confidence: 99%

AN Enhanced Model for Diabetes Prediction using Improved Firefly Feature Selection and Hybrid Random Forest Algorithm

Kumar¹,

Gunavathi²

2019

IJEAT

View full text Add to dashboard Cite

Diabetes is a chronic disease that causes numerous amount of death each year. Untreated diabetes disturbs the proper functionality of other organs in human body. Hence early detection is a significant process to have a healthy life style. Usually the performance of the classification is affected due to the existence of high dimensionality in medical data.In this study a system model is proposed on Pima dataset to enhance the classification accuracy by eliminating the irrelevant features. Therefore it is important to choose a suitable feature selection approach that provides the better accuracy in disease prediction compared to prior study.Hencenovel techniquesImprovedFirefly(IFF)andhybrid Random forest algorithmis proposed for feature selection and classification. The present study provides a better result with 96.3% accuracy.The efficiency of the present studyis compared with the prior classification approaches.

show abstract

“…A portion of the feature table created for training is shown in Table 2, where the class column can be seen. The support vector machine (SVM) is a supervised learning algorithm applied in countless fields to solve classification and regression problems [29]. Furthermore, following the literature [22,24,26], it has proven to be an effective technique to classify the surface roughness using higher dimensional data.…”

Section: Data Preparation and Model Trainingmentioning

confidence: 99%

A Novel Machine-Learning-Based Procedure to Determine the Surface Finish Quality of Titanium Alloy Parts Obtained by Heat Assisted Single Point Incremental Forming

Bautista-Monsalve

Garcı́a-Sevilla

Miguel

et al. 2021

Metals

View full text Add to dashboard Cite

Single point incremental forming (SPIF) is a cheap and flexible sheet metal forming process for rapid manufacturing of complex geometries. Additionally, it is important for engineers to measure the surface finish of work pieces to assess their quality and performance. In this paper, a predictive model based on machine learning and computer vision was developed to estimate arithmetic mean surface roughness (Ra) and maximum peak to valley height (Rz) of Ti6Al4V parts obtained by SPIF. An image database was prepared to train different classification algorithms in accordance with a supervised learning approach. A speeded up robust feature (SURF) detector was used to obtain visual vocabulary so that the classifiers are able to group the photographs into classes. The experimental results indicated that the proposed predictive method shows great potential to determine the surface quality, as classifiers based on a support vector machine with a polynomial kernel are suitable for this purpose.

show abstract

An imprecise extension of SVM-based machine learning models

Cited by 58 publications

References 31 publications

Forecasting Agricultural Commodity Prices Using Model Selection Framework With Time Series Features and Forecast Horizons

Forecasting Agricultural Commodity Prices Using Model Selection Framework With Time Series Features and Forecast Horizons

AN Enhanced Model for Diabetes Prediction using Improved Firefly Feature Selection and Hybrid Random Forest Algorithm

A Novel Machine-Learning-Based Procedure to Determine the Surface Finish Quality of Titanium Alloy Parts Obtained by Heat Assisted Single Point Incremental Forming

Contact Info

Product

Resources

About