Performance of KNN and SVM classifiers on full word Arabic articles

Hmeidi, Ismail; Hawashin, Bilal; El-Qawasmeh, Eyas

doi:10.1016/j.aei.2007.12.001

Cited by 117 publications

(60 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As the plant operates for more time, the quantity of recorded data for the PV plant is increasing, and the distribution of data in different weather situations categories may change as well. It is almost impossible for a single classification model to offer accurate weather status recognition results facing these problems [46]. Therefore, research on the performance of different classifiers in a variety of situations is warranted.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

et al. 2017

View full text Add to dashboard Cite

Accurate solar photovoltaic (PV) power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST) solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN) and support vector machines (SVM) are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Another work conducted a comparative study of two machine learning methods k nearest neighbor (KNN) and support vector machines (SVM) [9]. Full-word features was used and tf.idf as the weighting method for feature selection.…”

Section: Introductionmentioning

confidence: 99%

Classifying Arabic Text Using KNN Classifier

Al-Badarenah¹,

Al-Shawakfa²,

Al-Rababah³

et al. 2016

ijacsa

View full text Add to dashboard Cite

Abstract-With the tremendous amount of electronic documents available, there is a great need to classify documents automatically. Classification is the task of assigning objects (images, text documents, etc.) to one of several predefined categories. The selection of important terms is vital to classifier performance, feature set reduction techniques such as stop word removal, stemming and term threshold were used in this paper. Three term-selection techniques are used on a corpus of 1000 documents that fall in five categories. A comparison study is performed to find the effect of using full-word, stem, and the root term indexing methods. K-nearest -neighbors classifiers used in this study. The averages of all folds for Recall, Precision, Fallout, and Error-Rate were calculated. The results of the experiments carried out on the dataset show the importance of using k-fold testing since it presents the variations of averages of recall, precision, fallout, and error rate for each category over the 10-fold.

show abstract

“…In order to classify a testing article, it computes the distance between the article and all the training articles. More information about kNN can be obtained from Hmeidi et al [27] and Perrizo et al [28].…”

Section: K-nearest Neighborhood Algorithmmentioning

confidence: 99%

Bone age determination in young children (newborn to 6 years old) using support vector machines

Güraksın¹,

Uğuz²,

Baykan³

2016

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Abstract:Bone age is assessed through a radiological analysis of the left-hand wrist and is then compared to chronological age. A conflict between these two values indicates an abnormality in the development process of the skeleton. This study, conducted on children aged between 0 and 6 years, proposes a computer-based diagnostic system to eliminate the disadvantages of the methods used in bone age determination. For this purpose, primarily an image processing procedure was applied to the X-ray images of the left-hand wrist of children from different ethnic groups aged between 0 and 6 years. A total of 9 features, corresponding to the carpal bones and distal epiphysis of the radius bone with some physiological attributes of the children, were obtained. Then, by using gain ratio, the best 6 features were used for the classification process. Next, the bone age determination process was performed with the obtained features with the help of the support vector machine (SVM), naive Bayes, k-nearest neighborhood, and C4.5 algorithms. Finally, the features used in the determination process and their effects on the accuracies were examined. The results of the designed system showed that SVM method has a better achievement rate than the other methods at a rate of 72.82%. Additionally, in this study, a new feature corresponding to the distance between the centers of gravity of the carpal bones was used for the classification process, and the analysis of the related feature showed that there was a statistically significant difference at P <0.05 between this feature and bones in children aged between 0 and 6 years.

show abstract

Performance of KNN and SVM classifiers on full word Arabic articles

Cited by 117 publications

References 6 publications

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

Classifying Arabic Text Using KNN Classifier

Bone age determination in young children (newborn to 6 years old) using support vector machines

Contact Info

Product

Resources

About