Method of Feature Reduction in Short Text Classification Based on Feature Clustering

Li, Fangfang; Yin, Yao; Shi, Jinjing; Mao, Xingliang; Shi, Ronghua

doi:10.3390/app9081578

Cited by 7 publications

(3 citation statements)

References 30 publications

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“…The TF-IDF model is a prevalent research method in natural language processing (NLP) used in the implementation of the algorithm described in this article [20].…”

Section: ) Text Vectorizationmentioning

confidence: 99%

Freeway accident duration prediction based on social network information

Ji,

Li,

Chen

et al. 2022

NNW

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Accident duration prediction is the basis of freeway emergency management, and timely and accurate accident duration prediction can provide a reliable basis for road traffic diversion and rescue agencies. This study proposes a method for predicting the duration of freeway accidents based on social network information by collecting Weibo data of freeway accidents in Sichuan province and using the advantage that human language can convey multi-dimensional information. Firstly, text features are extracted through a TF-IDF model to represent the accident text data quantitatively; secondly, the variability between text data is exploited to construct an ordered text clustering model to obtain clustering intervals containing temporal attributes, thus converting the ordered regression problem into an ordered classification problem; finally, two nonparametric machine learning methods, namely support vector machine (SVM) and k-nearest neighbour method (KNN), to construct an accident duration prediction model. The results show that when the ordered text clustering model divides the text dataset into four classes, both the SVM model and the KNN model show better prediction results, and their average absolute error values are less than 22 %, which is much better than the prediction results of the regression prediction model under the same method.

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“…The TF-IDF model is a prevalent research method in natural language processing (NLP) used in the implementation of the algorithm described in this article [20].…”

Section: ) Text Vectorizationmentioning

confidence: 99%

Freeway accident duration prediction based on social network information

Ji,

Li,

Chen

et al. 2022

NNW

View full text Add to dashboard Cite

show abstract

“…This rate of increase of data production is also seen across many other fields such as graph data generated by social networks or finance data generated by the stock markets [5]. There is therefore a need for more reliable, cost-effective, and faster data mining techniques and machine learning models which use the data [6][7][8][9]. Useful features can be extracted from the data to reduce dimensionality by removing redundant or noisy features.…”

Section: Introductionmentioning

confidence: 97%

Cuda-Accelerated Feature Selection

Ramroach¹,

Herbert²,

Joshi³

2020

Proceedings of the International Conference on Emerging Trends in Engineering &Amp; Technology (IConETech-2020)

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Identifying important features from high dimensional data is usually done using one-dimensional filtering techniques. These techniques discard noisy attributes and those that are constant throughout the data. This is a time-consuming task that has scope for acceleration via high performance computing techniques involving the graphics processing unit (GPU). The proposed algorithm involves acceleration via the Compute Unified Device Architecture (CUDA) framework developed by Nvidia. This framework facilitates the seamless scaling of computation on any CUDA-enabled GPUs. Thus, the Pearson Correlation Coefficient can be applied in parallel on each feature with respect to the response variable. The ranks obtained for each feature can be used to determine the most relevant features to select. Using data from the UCI Machine Learning Repository, our results show an increase in efficiency for multi-dimensional analysis with a more reliable feature importance ranking. When tested on a high-dimensional dataset of 1000 samples and 10,000 features, we achieved a 1,230-time speedup using CUDA. This acceleration grows exponentially, as with any embarrassingly parallel task.

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“…Among them, short text classification has attracted more attention and encountered more challenges due to their limited length. Most of the internet data are short texts, such as microblogs and bulletin board system (BBS) [5]. Accurate short text classification plays an important role both for the enterprises and individuals, even for the government services.…”

Section: Introductionmentioning

confidence: 99%

DUKMSVM: A Framework of Deep Uniform Kernel Mapping Support Vector Machine for Short Text Classification

et al. 2020

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This paper mainly deals with the problem of short text classification. There are two main contributions. Firstly, we introduce a framework of deep uniform kernel mapping support vector machine (DUKMSVM). The significant merit of this framework is that by expressing the kernel mapping function explicitly with a deep neural network, it is in essence an explicit kernel mapping instead of the traditional kernel function, and it allows better flexibility in dealing with various applications by applying different neural network structures. Secondly, to validate the effectiveness of this framework and to improve the performance of short text classification, we explicitly express the kernel mapping using bidirectional recurrent neural network (BRNN), and propose a deep bidirectional recurrent kernel mapping support vector machine (DRKMSVM) for short text classification. Experimental results on five public short text classification datasets indicate that in terms of classification accuracy, precision, recall rate and F1-score, the DRKMSVM achieves the best performance with the average values of accuracy, precision, recall rate, and F1-score of 87.23%, 86.99%, 86.13% and 86.51% respectively compared to traditional SVM, convolutional neural network (CNN), Naive Bayes (NB), and Deep Neural Mapping Support Vector Machine (DNMSVM) which applies multi-layer perceptron for kernel mapping.

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Method of Feature Reduction in Short Text Classification Based on Feature Clustering

Cited by 7 publications

References 30 publications

Freeway accident duration prediction based on social network information

Freeway accident duration prediction based on social network information

Cuda-Accelerated Feature Selection

DUKMSVM: A Framework of Deep Uniform Kernel Mapping Support Vector Machine for Short Text Classification

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