A fuzzy support vector machine algorithm for classification based on a novel PIM fuzzy clustering method

Wu, Zhenlong; Zhang, Huaguang; Liu, Jinhai

doi:10.1016/j.neucom.2012.07.049

Cited by 53 publications

(31 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The Support Vector Machine algorithm is supervised learning approach used to solve classification problems. It accepts labelled training data and produces hyperplane which is used to maximize the margin between high-dimensional space classes (Wu et al, 2014). The Decision Forest algorithm is a learning method consisting of multiple classification methods.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

Harfoushi¹,

Hasan²,

Obiedat³

2018

MAS

View full text Add to dashboard Cite

The Sentimental Analysis (SA) is a widely known and used technique in the natural language processing realm. It is often used in determining the sentiment of a text. It can be used to perform social media analytics. This study sought to compare two algorithms; Logistic Regression, and Support Vector Machine (SVM) using Microsoft Azure Machine Learning. This was demonstrated by performing a series of experiments on three Twitter datasets (TD). Accordingly, data was sourced from Twitter a microblogging platform. Data were obtained in the form of individuals' opinions, image, views, and twits from Twitter. Azure cloud-based sentiment analytics models were created based on the two algorithms. This work was extended with more in-depth analysis from another Master research conducted lately. Results confirmed that Microsoft Azure ML platform can be used to build effective SA models that can be used to perform data analytics.

show abstract

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

Harfoushi¹,

Hasan²,

Obiedat³

2018

MAS

View full text Add to dashboard Cite

show abstract

“…al., [5] have proposed a PIMclustering-based FSVM algorithm for classification problems with outliers or noises. The experiments have been conducted on five benchmark datasets to test the generalization performance of the PIM-FSVM algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Filter Based Algorithms for Selecting Optimal Features From Thyroid Disease Dataset

Pavya¹

2017

ijarcs

View full text Add to dashboard Cite

Abstract:In medical science, automatic disease diagnosis is an invaluable tool because of restricted observation of the specialist and uncertainties in medical knowledge. Advances in medical information technology have enabled healthcare industries to automatically collect huge amount of data through clinical laboratory examinations. To explore these data, the past few years have envisaged the use of Computer Aided Diagnosis (CAD) systems in many screening sites and hospitals. While using CAD, thyroid function diagnosis is considered as a classification problem, which can automatically identify the type of thyroid (hyper, hypo or normal). Machine learning techniques are increasingly introduced to construct the CAD systems owing to its strong capability of extracting complex relationships in the biomedical data.

show abstract

“…Note that the variable h in (7) and that in (5) refer to the same functional. By minimizing h in (7), we are minimizing an exact bound on γ, the VC dimension of the classifier.…”

Section: The Linear Minimal Complexity Machinementioning

confidence: 99%

“…The most commonly used variants are the maximum margin L 1 norm SVM [1], and the least squares SVM (LSSVM) [2], both of which require the solution of a quadratic programming problem. In the last few years, SVMs have been applied to a number of applications to obtain cutting edge performance; novel uses have also been devised, where their utility has been amply demonstrated [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]. SVMs were motivated by the celebrated work of Vapnik and his colleagues on generalization, and the complexity of learning.…”

Section: Introductionmentioning

confidence: 99%

Learning a hyperplane classifier by minimizing an exact bound on the VC dimension1

Jayadeva

2015

Neurocomputing

View full text Add to dashboard Cite

The VC dimension measures the complexity of a learning machine, and a low VC dimension leads to good generalization. While SVMs produce state-of-the-art learning performance, it is well known that the VC dimension of a SVM can be unbounded; despite good results in practice, there is no guarantee of good generalization. In this paper, we show how to learn a hyperplane classifier by minimizing an exact, or Θ bound on its VC dimension. The proposed approach, termed as the Minimal Complexity Machine (MCM), involves solving a simple linear programming problem. Experimental results show, that on a number of benchmark datasets, the proposed approach learns classifiers with error rates much less than conventional SVMs, while often using fewer support vectors. On many benchmark datasets, the number of support vectors is less than one-tenth the number used by SVMs, indicating that the MCM does indeed learn simpler representations.

show abstract

A fuzzy support vector machine algorithm for classification based on a novel PIM fuzzy clustering method

Cited by 53 publications

References 23 publications

Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

Enhancing Filter Based Algorithms for Selecting Optimal Features From Thyroid Disease Dataset

Learning a hyperplane classifier by minimizing an exact bound on the VC dimension1

Contact Info

Product

Resources

About