Comparing SVM and na&amp;#x00EF;ve Bayes classifiers for text categorization with Wikitology as knowledge enrichment

Hassan, Sundus; Rafi, Muhammad; Shaikh, M. Shahid

doi:10.1109/inmic.2011.6151495

Cited by 42 publications

(18 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The use of twitter media as an ingredient for the case of land occupancy based on weather conditions with the algorithm naïve bayes classifier and SVM presented by Marchel et al [15], obtained results where the SVM method has a greater level of performance than naive bayes with a difference in accuracy of 0.0015 due to inability to naive bayes in overcoming data outlier problems. There are previous studies that also use 2 algorithms namely Naïve Bayes Classifier and SVM presented by Hassan et al [16]. This research was carried out under text enrichment through Wikitology using data from 20 newsgroups with 1000 categories, the results showed that the more training data, the greater the accuracy of SVM, the more features and external enrichment for labeling, the greater the accuracy of Naïve Bayes.…”

Section: Literature Reviewmentioning

confidence: 97%

“…Immigrant schemes are used to overcome dynamic location routing (DLRP) problems with ant colony with clustering (KACO), the results of the study show that compared to normal ACO algorithms such as US, EAS, MMAS, ACS, P-ACO, SA and GA, KACO produce better performance. This study will use the approach taken by Gao et al [17] to address the problem of dynamic travel route selection and in-depth learning to detect traffic accidents from social media data as done by Hasan et al [16] using twitter data around events on the road with methods Support Vector Machine. The time-based data will be weighted by the SAW method so that the visibility value is obtained as the basis for calculating the probability node to determine the fastest route in real-time with the Ant Colony algorithm and applied to the problem of fire truck transportation.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Ant Colony Algorithm for Determining Dynamic Travel Routes Based on Traffic Information from Twitter

2019

View full text Add to dashboard Cite

Combining the search method for fire suppression routes with ant colony algorithms and methods of analyzing twitter events on the highway is the basis of the problems to be studied. The results of the twitter data feature extraction are classified with Support Vector Machine after it is implemented in the Simple Additive Weighting method in calculating path weights with criteria of distance, congestion, multiple branching, and many holes. Line weights are also used as initial pheromone values. The C-means method is used to group the weights of each path and distance so that the path with the lowest weight and the shortest distance that will be simulated using the Ant Colony. The validation results with cross fold on SVM with linear kernels produce the greatest accuracy value is 97.93% for training data distribution: test data 6: 4. The simulation of the selection of the damkar car path from Feather to Pleburan with Ant Colony obtained 50 seconds of computation time, whereas with Ant Colony with Clustering the computation time was 15, resulting in a reduction in computing of 35. Ant colony with MinMax optimization gives the best computation time of 14.47 seconds with 100 iterations and 10 nodes.

show abstract

Section: Literature Reviewmentioning

confidence: 97%

Section: Literature Reviewmentioning

confidence: 99%

Ant Colony Algorithm for Determining Dynamic Travel Routes Based on Traffic Information from Twitter

2019

View full text Add to dashboard Cite

show abstract

“…Sundus Hassan et.al. [22] proposed a method for text categorization in which they compared Support Vector Machine(SVM) and Naive Bayes (NB) classifiers. Baseline for the experiment has setup by removing stopwords and stemmed the dataset by using Porter Stemmer.…”

Section: Reviewed Papers In This Directionmentioning

confidence: 99%

Machine Learning: A Review on Binary Classification

Kumari¹,

Srivastava²

2017

IJCA

110

View full text Add to dashboard Cite

In the field of information extraction and retrieval, binary classification is the process of classifying given document/account on the basis of predefined classes. Sockpuppet detection is based on binary, in which given accounts are detected either sockpuppet or non-sockpuppet. Sockpuppets has become significant issues, in which one can have fake identity for some specific purpose or malicious use. Text categorization is also performed with binary classification. This research synthesizes binary classification in which various approaches for binary classification are discussed.

show abstract

“…Naive Bayes classifier is an elementary classifier that is suitable in case of large set of input data and it outperforms other sophisticated algorithms [18]. Naive Bayes have been proved to be a better choice for text classification, compared to other algorithms [8], [9]. It is a probabilistic classifier which applies Bayes theorem and assumes conditional independence of attributes.…”

Section: Background Theorymentioning

confidence: 99%

Improved document classification through enhanced Naive Bayes algorithm

Sathyadevan

Sarath

Athira

et al. 2014

2014 International Conference on Data Science &Amp; Engineering (ICDSE)

View full text Add to dashboard Cite

Immense growth in communication has paved way for existence of information across the world in wide separated zones. There exists a need for a mechanism to render apt information to the needy from this enormous source of information. This mechanism is of high demand for educational purposes. Knowledge based cloud (Kloud) proposes a solution to combine together the information in different area, which is managed by several organizations. It then organizes them into different sections and hence providing a platform to furnish relevant information to people in search of it. The paper discusses about a method based on Naive Bayes algorithm to classify documents pushed into "Kloud". A variation to this algorithm has been implemented by calculating term weight using "converged weight" method resulting in better accuracy and speed. A comparative study of proposed variance in classification algorithm against the actual algorithm was performed. Further we also implemented two subclassification algorithms namely hierarchical subclassification and subcategorization using document similarity method

show abstract

Comparing SVM and naïve Bayes classifiers for text categorization with Wikitology as knowledge enrichment

Cited by 42 publications

References 5 publications

Ant Colony Algorithm for Determining Dynamic Travel Routes Based on Traffic Information from Twitter

Ant Colony Algorithm for Determining Dynamic Travel Routes Based on Traffic Information from Twitter

Machine Learning: A Review on Binary Classification

Improved document classification through enhanced Naive Bayes algorithm

Contact Info

Product

Resources

About