A Comprehensive Survey on Support Vector Machine in Data Mining Tasks: Applications &amp; Challenges

Nayak, Janmenjoy; Naik, Bighnaraj; Behera, Himansu Sekhar

doi:10.14257/ijdta.2015.8.1.18

Cited by 125 publications

(63 citation statements)

References 93 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Experimental results show that the method performs better than tf-idf with/without stops words and word2vec with/without stop words. A major drawback in their work is that, stops words increase the dimensionality of the feature vectors which impacts badly on the classification accuracy and computational burden [8].Also the classification algorithm used was a linear SVM, other kernels such as string and RBF kernels could produce better results [20].…”

Section: Related Work 21 Web Page Classificationmentioning

confidence: 99%

A Neural Network Language Document Representation Technique for Web-Page Classification

Quadri¹,

Ajose-Ismail²

2020

IJCA

View full text Add to dashboard Cite

Section: Related Work 21 Web Page Classificationmentioning

confidence: 99%

A Neural Network Language Document Representation Technique for Web-Page Classification

Quadri¹,

Ajose-Ismail²

2020

IJCA

View full text Add to dashboard Cite

“…Support vector machine (SVM) is a widely adopted binary classifier in recent years due to its efficiency and accuracy [10,39]. As a supervised classification algorithm, SVM uses labeled training data to build a model and infers the two categories of the testing data.…”

Section: Background On Svmmentioning

confidence: 99%

Feature Design for Protein Interface hotspots using KFC2 and Rosetta

Seeger¹,

Little²,

Chen³

et al. 2019

Preprint

View full text Add to dashboard Cite

Protein-protein interactions regulate many essential biological processes and play an important role in health and disease. The process of experimentally characterizing protein residues that contribute the most to protein-protein interaction affinity and specificity is laborious. Thus, developing models that accurately characterize hotspots at protein-protein interfaces provides important information about how to inhibit therapeutically relevant protein-protein interactions. During the course of the ICERM WiSDM workshop 2017, we combined the KFC2a protein-protein interaction hotspot prediction features with Rosetta scoring function terms and interface filter metrics. A 2-way and 3-way forward selection strategy was employed to train support vector machine classifiers, as was a reverse feature elimination strategy. From these results, we identified subsets of KFC2a and Rosetta combined features that show improved performance over KFC2a features alone.

show abstract

“… Support Vector Machine: -SVMs, being computationally powerful tools for supervised learning, are widely used in classification [10,11].…”

Section: Neural Net (Mlp)mentioning

confidence: 99%

Student Performance Prediction Model using Machine Learning Approach: The Case of Wolkite University

Belachew¹,

Gobena²

2017

IJARCSSE

View full text Add to dashboard Cite

Abstract-A high prediction accuracy of the students' performance is helpful to identify the low performance students at the beginning of the learning process. Machine learning is used to attain this objective. Machine learning techniques are used to discover models or patterns of data, and it is helpful in the decision-making. The ability to predict performance of students is very crucial in our present education system. We applied Machine learning concepts for this study. The dataset used in our study is taken from the Wolkite university registries office for college of computing and informatics from 2004 up to 2007 E.C with respect to each department. In this study, we have been collected student's transcript data that included their final GPA and their grades in all courses. After pre-processing the data, we applied the machine learning methods, neural networks, Naive Bayesian and Support Vector Machine (SMO). Finally, we built the model for each method, evaluate the performance and compare the results of each model. Using machine learning, the aim was to develop a model which can derive the conclusion on students' academic success.

show abstract

A Comprehensive Survey on Support Vector Machine in Data Mining Tasks: Applications & Challenges

Abstract: Abstract

Cited by 125 publications

References 93 publications

A Neural Network Language Document Representation Technique for Web-Page Classification

A Neural Network Language Document Representation Technique for Web-Page Classification

Feature Design for Protein Interface hotspots using KFC2 and Rosetta

Student Performance Prediction Model using Machine Learning Approach: The Case of Wolkite University

Contact Info

Product

Resources

About