Latent‐lSVM classification of very high‐dimensional and large‐scale multi‐class datasets

Nghi, Thanh; Poulet, F.

doi:10.1002/cpe.4224

Cited by 11 publications

(10 citation statements)

References 64 publications

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“…The justification of the generalization ability is based on the comparison between Equation (6) of the global SVR model trained for the full dataset and Equation (7) of the local SVR model trained from a terminal-node. According to Theorem 1 in Do and Poulet (2016b) and Theorem 2, 3 in Do and Poulet (2017), the margin size D X k of the local SVR model is greater than the margin size D X of the global one. Nevertheless, the use of the terminal-node (subset) in the training task of tSVR leads to R k ≤ R and m k ≤ m. These allow concluding that there is a compromise between the locality (i.e.…”

Section: Performance Analysismentioning

confidence: 95%

Decision trees using local support vector regression models for large datasets

Tran-Nguyen

Bui

2019

Journal of Information and Telecommunication

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Our proposed decision trees using local support vector regression models (tSVR, rtSVR) aim to efficiently handle the regression task for large datasets. The learning algorithm tSVR of regression models is done by two main steps. The first one is to construct a decision tree regressor for partitioning the full training dataset into k terminal-nodes (subsets), followed which the second one is to learn the SVR model from each terminal-node to predict the data locally in a parallel way on multi-core computers. The algorithm rtSVR learns the random forest of decision trees with local SVR models for improving the prediction correctness against the tSVR model alone. The performance analysis shows that our algorithms tSVR, rtSVR are efficient in terms of the algorithmic complexity and the generalization ability compared to the classical SVR. The experimental results on five large datasets from UCI repository showed that proposed tSVR and rtSVR algorithms are faster than the standard SVR in training the non-linear regression model from large datasets while achieving the high correctness in the prediction. Typically, the average training time of tSVR and rtSVR are 1282.66 and 482.29 times faster than the standard SVR; Furthermore, tSVR and rtSVR improve 59.43%, 63.70% of the relative prediction correctness compared to the standard SVR. ARTICLE HISTORY

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Section: Performance Analysismentioning

confidence: 95%

Decision trees using local support vector regression models for large datasets

Tran-Nguyen

Bui

2019

Journal of Information and Telecommunication

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“…There exist various variants of SVMs [14]- [19]. These are the state-of-the-art SVM models that have been used for text classification.…”

Section: What Is Support Vector Machines and Ensemble Of Supportmentioning

confidence: 99%

“…SVM based classifiers have proved to be a viable option for larges scale text classification problems. In the work of Do et al [19], a latent SVM based text classification approach is proposed that works fine with large data sets.…”

Section: What Is Support Vector Machines and Ensemble Of Supportmentioning

confidence: 99%

Text Classification Using Ensemble Of Non-Linear Support Vector Machines

Sharma¹,

Sharma²

2019

IJITEE

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With the advent of digital era, billions of the documents generate every day that need to be managed, processed and classified. Enormous size of text data is available on world wide web and other sources. As a first step of managing this mammoth data is the classification of available documents in right categories. Supervised machine learning approaches try to solve the problem of document classification but working on large data sets of heterogeneous classes is a big challenge. Automatic tagging and classification of the text document is a useful task due to its many potential applications such as classifying emails into spam or non-spam categories, news articles into political, entertainment, stock market, sports news, etc. The paper proposes a novel approach for classifying the text into known classes using an ensemble of refined Support Vector Machines. The advantage of proposed technique is that it can considerably reduce the size of the training data by adopting dimensionality reduction as pre-training step. The proposed technique has been used on three bench-marked data sets namely CMU Dataset, 20 Newsgroups Dataset, and Classic Dataset. Experimental results show that proposed approach is more accurate and efficient as compared to other state-of-the-art methods.

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“…Canuto et al presented a use of a multiobjective optimization strategy to reduce the number of metafeatures while maximizing the classification effectiveness. Do and Poulet proposed a novel fast and accurate parallel local support vector machine (SVM) algorithm for classifying very high‐dimensional input spaces and large‐scale multiclass data sets. Gao et al developed a structured sparse representation classifier to short TC.…”

Section: Introductionmentioning

confidence: 99%

An improved term weighting scheme for text classification

Zhong

2019

Concurrency and Computation

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Summary Text representation is a necessary and primary procedure in performing text classification (TC), which first needs to be obtained through an information‐rich term weighting scheme to achieve higher TC performance. So far, term frequency‐inverse document frequency (TF‐IDF) is the most widely used term weighting scheme, but it suffers from two deficiencies. First, the global weighting factors IDF in TF‐IDF approaches infinity if a certain term does not occur in a text. Second, the IDF is equal to zero if a certain term appears in any text. To offset these drawbacks, we first conduct an in‐depth analysis of the current term weighting schemes, and subsequently, an improved term weighting scheme called term frequency‐inverse exponential frequency (TF‐IEF) and its various variants are proposed. The proposed method replaces IDF with the new global weighting factor IEF to characterize the global weighting factor log‐like IDF in the corpus, which can greatly reduce the effect of feature (term) with high local weighting factor TF in term weighting. As a result, a more representative feature can be generated. We carried out a series of experiments on two commonly used data sets (corpora) utilizing Naïve Bayes and support vector machine classifiers to validate the performance of our proposed schemes. Experimental results explicitly reveal that the proposed term weighting schemes come with better performance than the compared schemes.

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Latent‐lSVM classification of very high‐dimensional and large‐scale multi‐class datasets

Cited by 11 publications

References 64 publications

Decision trees using local support vector regression models for large datasets

Decision trees using local support vector regression models for large datasets

Text Classification Using Ensemble Of Non-Linear Support Vector Machines

An improved term weighting scheme for text classification

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