Supervised learning of sparse context reconstruction coefficients for data representation and classification

Liu, Xuejie; Wang, Jingbin; Yin, Ming; Edwards, Benjamin; Xu, Pengfei

doi:10.1007/s00521-015-2042-5

Cited by 22 publications

(19 citation statements)

References 48 publications

(48 reference statements)

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“…k-NNs are also most popular for classifying instances based on the context of data points through majority voting(X. Liu, Wang, Yin, Edwards, & Xu, 2017). This method is highly suitable for small datasets.…”

Section: K-nearest Neighbormentioning

confidence: 99%

Text Classification Techniques: A Literature Review

Thangaraj

Sivakami

2018

IJIKM

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Aim/Purpose: The aim of this paper is to analyze various text classification techniques employed in practice, their strengths and weaknesses, to provide an improved awareness regarding various knowledge extraction possibilities in the field of data mining. Background: Artificial Intelligence is reshaping text classification techniques to better acquire knowledge. However, in spite of the growth and spread of AI in all fields of research, its role with respect to text mining is not well understood yet. Methodology: For this study, various articles written between 2010 and 2017 on “text classification techniques in AI”, selected from leading journals of computer science, were analyzed. Each article was completely read. The research problems related to text classification techniques in the field of AI were identified and techniques were grouped according to the algorithms involved. These algorithms were divided based on the learning procedure used. Finally, the findings were plotted as a tree structure for visualizing the relationship between learning procedures and algorithms. Contribution: This paper identifies the strengths, limitations, and current research trends in text classification in an advanced field like AI. This knowledge is crucial for data scientists. They could utilize the findings of this study to devise customized data models. It also helps the industry to understand the operational efficiency of text mining techniques. It further contributes to reducing the cost of the projects and supports effective decision making. Findings: It has been found more important to study and understand the nature of data before proceeding into mining. The automation of text classification process is required, with the increasing amount of data and need for accuracy. Another interesting research opportunity lies in building intricate text data models with deep learning systems. It has the ability to execute complex Natural Language Processing (NLP) tasks with semantic requirements. Recommendations for Practitioners: Frame analysis, deception detection, narrative science where data expresses a story, healthcare applications to diagnose illnesses and conversation analysis are some of the recommendations suggested for practitioners. Recommendation for Researchers: Developing simpler algorithms in terms of coding and implementation, better approaches for knowledge distillation, multilingual text refining, domain knowledge integration, subjectivity detection, and contrastive viewpoint summarization are some of the areas that could be explored by researchers. Impact on Society: Text classification forms the base of data analytics and acts as the engine behind knowledge discovery. It supports state-of-the-art decision making, for example, predicting an event before it actually occurs, classifying a transaction as ‘Fraudulent’ etc. The results of this study could be used for developing applications dedicated to assisting decision making processes. These informed decisions will help to optimize resources and maximize benefits to the mankind. Future Research: In the future, better methods for parameter optimization will be identified by selecting better parameters that reflects effective knowledge discovery. The role of streaming data processing is still rarely explored when it comes to text classification.

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Section: K-nearest Neighbormentioning

confidence: 99%

Text Classification Techniques: A Literature Review

Thangaraj

Sivakami

2018

IJIKM

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“…The experiments over some benchmark databases show its advantages over other similarity learning methods. In the future, we will investigate to use some other similarity function as similarity measure instead of linear function, such as Bayesian network [12], [13], [14], and also to develop novel algorithms of other machine learning problems and applications besides similarity learning, to maximize top precision measure, such as importance sampling [15], [16], [17], portfolio choices [18], [19], multimedia technology [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], computational biology [30], [31], [32], [33], [34], big data processing [35], [36], [37], [38], [39], computer vision [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], information security [54], [55], [56]…”

Section: Discussionmentioning

confidence: 99%

Optimizing top precision performance measure of content-based image retrieval by learning similarity function

Liang

Shi²,

Wang

et al. 2016

2016 23rd International Conference on Pattern Recognition (ICPR)

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Abstract-In this paper we study the problem of content-based image retrieval. In this problem, the most popular performance measure is the top precision measure, and the most important component of a retrieval system is the similarity function used to compare a query image against a database image. However, up to now, there is no existing similarity learning method proposed to optimize the top precision measure. To fill this gap, in this paper, we propose a novel similarity learning method to maximize the top precision measure. We model this problem as a minimization problem with an objective function as the combination of the losses of the relevant images ranked behind the top-ranked irrelevant image, and the squared Frobenius norm of the similarity function parameter. This minimization problem is solved as a quadratic programming problem. The experiments over two benchmark data sets show the advantages of the proposed method over other similarity learning methods when the top precision is used as the performance measure.

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“…Majority voting [28] is a commonly used combination technique. The ensemble classifier predicts a class for an instance using the majority of base classifiers [29, 30]. The classification phase employs only feed forward based on (1) and (2) to do the classification.…”

Section: Algorithm Designmentioning

confidence: 99%

Parallelizing Backpropagation Neural Network Using MapReduce and Cascading Model

Liu

Jing

2016

Computational Intelligence and Neuroscience

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Artificial Neural Network (ANN) is a widely used algorithm in pattern recognition, classification, and prediction fields. Among a number of neural networks, backpropagation neural network (BPNN) has become the most famous one due to its remarkable function approximation ability. However, a standard BPNN frequently employs a large number of sum and sigmoid calculations, which may result in low efficiency in dealing with large volume of data. Therefore to parallelize BPNN using distributed computing technologies is an effective way to improve the algorithm performance in terms of efficiency. However, traditional parallelization may lead to accuracy loss. Although several complements have been done, it is still difficult to find out a compromise between efficiency and precision. This paper presents a parallelized BPNN based on MapReduce computing model which supplies advanced features including fault tolerance, data replication, and load balancing. And also to improve the algorithm performance in terms of precision, this paper creates a cascading model based classification approach, which helps to refine the classification results. The experimental results indicate that the presented parallelized BPNN is able to offer high efficiency whilst maintaining excellent precision in enabling large-scale machine learning.

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Supervised learning of sparse context reconstruction coefficients for data representation and classification

Cited by 22 publications

References 48 publications

Text Classification Techniques: A Literature Review

Text Classification Techniques: A Literature Review

Optimizing top precision performance measure of content-based image retrieval by learning similarity function

Parallelizing Backpropagation Neural Network Using MapReduce and Cascading Model

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