Classification of tweets data based on polarity using improved RBF kernel of SVM

Gopi, Arepalli Peda; Jyothi, R.; Narayana, V. Lakshman; Sandeep, Kanumalli Satya

doi:10.1007/s41870-019-00409-4

Cited by 112 publications

(52 citation statements)

References 21 publications

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“…The theoretical time and space complexities of the SVM model defined here based on various sources [34] [35]. The complexity of SVM mainly depends on the amounts of sampling data.…”

Section: Time Complexitymentioning

confidence: 99%

WITHDRAWN: Exploring the Effectiveness of Convolutional Neural Network with Ensemble Technique

Ghosh¹,

Sanyal²

2020

Preprint

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Sentiment Analysis has recently been considered as the most active research field in the natural language processing (NLP) domain. Deep Learning is a subset of the large family of Machine Learning and becoming a growing trend due to its automatic learning capability with impressive consequences across different NLP tasks. Hence, a fusion-based Machine Learning framework has been attempted by merging the Traditional Machine Learning method with Deep Learning techniques to tackle the challenge of sentiment prediction for a massive amount of unstructured review dataset. The proposed architecture aims to utilize the Convolutional Neural Network (CNN) with a backpropagation algorithm to extract embedded feature vectors from the top hidden layer. Thereafter, these vectors augmented to an optimized feature set generated from binary particle swarm optimization (BPSO) method. Finally, a traditional SVM classifier is trained with these extended features set to determine the optimal hyper-plane for separating two classes of review datasets. The evaluation of this research work has been carried out on two benchmark movie review datasets IMDB, SST2. Experimental results with comparative studies based on performance accuracy and F-score value are reported to highlight the benefits of the developed frameworks.

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“…The theoretical time and space complexities of the SVM model defined here based on various sources [34] [35]. The complexity of SVM mainly depends on the amounts of sampling data.…”

Section: Time Complexitymentioning

confidence: 99%

WITHDRAWN: Exploring the Effectiveness of Convolutional Neural Network with Ensemble Technique

Ghosh¹,

Sanyal²

2020

Preprint

View full text Add to dashboard Cite

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“…Classification is a predictive-based data mining task. In order to accomplish the classification task, the classification algorithm is used to learn the dataset and to build the classifier [15]. The dataset contains a set of features (columns) and instances (rows) with a target-class attribute which contains the class-label associated with each instance of the dataset [16].…”

Section: 6 Represents the Schematic Representation Of Classificationmentioning

confidence: 99%

“…Gopi et al [15] utilized a piece of the learning procedure of the regulated learning calculation for feature determination. Inserted based strategies lessen the computational expense than the wrapper strategy.…”

Section: Literature Workmentioning

confidence: 99%

Machine Learning Methodology for Medical Data Analysis for Prediction of Risk

Sravanthi¹,

Harika²,

Archana³

et al. 2020

jcr

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Mining data is a nontrivial procedure of finding information from a large volume of data. Such information can be helpful in settling on significant choices. Medical data show special features including noise coming about because of human just as methodical blunders, missing qualities and even meager conditions. The nature of data has huge ramifications for the nature of the mining results. Medical data classification is important to perform preprocessing steps so as to expel or at least lighten a portion of the issues related with medical data. Clustering is a descriptive-based data mining task. The clustering algorithm is also called as unsupervised learning algorithm that learns the unlabeled dataset and groups or clusters the instances based on their similarity and builds the clustering model. Clustering is same as classification in which data is grouped, but in this, groups are not predefined. In clustering, clusters are not predefined. Classification of different types of clustering is as follows: Hierarchical clustering, Partition clustering, Categorical clustering, Density based clustering and Grid based clustering. The main intension of the research is to classify the medical data with high accuracy value. In order to achieve promising results, a novel data classification methods have been designed that utilize a Improved Cluster Optimal Classifier (ICOC). The proposed method is compared with traditional methods and the results show that the proposed method performance is better and accurate.

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“…Be that as it may, this system was likewise constrained to numerous SVMs with the direct kernel and with similar dimensionality of selected subsets with dynamic highlights [9].…”

Section: Fig 1: Kernel Function Flowmentioning

confidence: 99%

Imbalanced Data in Sensible Kernel Space With Support Vector Machines Multiclass Classifier Design

Lakshmi¹,

Vasanthi²,

Suneetha³

et al. 2020

jcr

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The utilization of various assessment measures for grouping different tasks have picked up a lot of consideration in previous decades extraordinarily for such issues through different and redundant classes. A classifier is proposed particularly to advance one of the conceivable measures, to be specific, the hypothetical G-mean method. In any case, the method is general, and it very well may be utilized to streamline bland assessment measures. The streamlining calculation to prepare the classifier is depicted, and the numerical plan is tried demonstrating its ease of use and power. The proposed oversampling calculation alongside a cost-reduction SVM classification is appeared to enhance execution when contrasted with other estimated strategies on numerous benchmark imbalanced informational indexes. What's more, a various leveled system is produced for multiclass imbalanced issues that have a dynamic class arrange. A novel structure for kernel space instruction in a limited space named Sensible Kernel Space (SKS) is introduced in this manuscript. The SKS can be expressly worked by utilizing any optimistic clear bit counting Gaussian BCG bit by means of an exact portion outlined. The proposed sensible Kernel space can ideally choose various subsets of recently mapped datasets in SKS keeping in mind the end goal to enhance the speculation execution of the classifier.

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Classification of tweets data based on polarity using improved RBF kernel of SVM

Cited by 112 publications

References 21 publications

WITHDRAWN: Exploring the Effectiveness of Convolutional Neural Network with Ensemble Technique

WITHDRAWN: Exploring the Effectiveness of Convolutional Neural Network with Ensemble Technique

Machine Learning Methodology for Medical Data Analysis for Prediction of Risk

Imbalanced Data in Sensible Kernel Space With Support Vector Machines Multiclass Classifier Design

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