Efficient classification of multi-labeled text streams by clashing

Ñanculef, Ricardo; Flaounas, Ilias; Cristianini, Nello

doi:10.1016/j.eswa.2014.02.017

Cited by 24 publications

(9 citation statements)

References 68 publications

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“…(16), (17) and (18) In this paper, we randomly select 2000 entries as experimental dataset from a dataset released by Hylanda Information Technology Co. Ltd. in Tianjin of China which is comprised of almost all news report containing keywords of public security events. In the dataset, the public security events are divided into three types: violent terrorist attacks, campus attacks, and explosions.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…VSM, firstly put forward by Dr. Salton in 1968, uses vector to represent texts, which has always been the most classic calculation for text representation [17,18]. Its idea originates from the fact that all the texts and queries contain some independent property that is expressed by some characteristic items to reveal their contents and that can be regarded as one dimension of vector space; thus the texts and queries can be expressed as the collection of these attributes ignoring complicated relation among paragraphs, sentences, and words.…”

Section: Text Representation Modelmentioning

confidence: 99%

“…(2) All of the above document streams are firstly partitioned into segments by a piece of software for words segmentation named ICTCLAS developed by Chinese Academy of Sciences which is one of the most popular tools used for words segmentation. (3) The TF-IDF method [17] is adopted to calculate the weight of each keyword. And we use classical vector space model (VSM) [18] to extract main points of each sample.…”

Section: System Design and Modelmentioning

confidence: 99%

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An Improved Clustering Method for Detection System of Public Security Events Based on Genetic Algorithm and Semisupervised Learning

et al. 2017

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The occurrence of series of events is always associated with the news report, social network, and Internet media. In this paper, a detecting system for public security events is designed, which carries out clustering operation to cluster relevant text data, in order to benefit relevant departments by evaluation and handling. Firstly, texts are mapped into three-dimensional space using the vector space model. Then, to overcome the shortcoming of the traditional clustering algorithm, an improved fuzzy -means (FCM) algorithm based on adaptive genetic algorithm and semisupervised learning is proposed. In the proposed algorithm, adaptive genetic algorithm is employed to select optimal initial clustering centers. Meanwhile, motivated by semisupervised learning, guiding effect of prior knowledge is used to accelerate iterative process. Finally, simulation experiments are conducted from two aspects of qualitative analysis and quantitative analysis, which demonstrate that the proposed algorithm performs excellently in improving clustering centers, clustering results, and consuming time.

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Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Text Representation Modelmentioning

confidence: 99%

Section: System Design and Modelmentioning

confidence: 99%

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An Improved Clustering Method for Detection System of Public Security Events Based on Genetic Algorithm and Semisupervised Learning

et al. 2017

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“…In this case, the multi-label text data is obtained, so analysis becomes more complex. There are various researches where the multi-label text data has been analysed using different techniques, but the class adjustment and verification are not considered (Nanculef et al, 2014;Park and Lee, 2008). The main problem of multi-label text data class verification and adjustment is deciding which class of the data item should be changed and which class should be assigned instead.…”

Section: Introductionmentioning

confidence: 99%

Approach for Multi-Label Text Data Class Verification and Adjustment Based on Self-Organizing Map and Latent Semantic Analysis

Stefanovič¹,

Kurasova²

2022

Informatica

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In this paper, a new approach has been proposed for multi-label text data class verification and adjustment. The approach helps to make semi-automated revisions of class assignments to improve the quality of the data. The data quality significantly influences the accuracy of the created models, for example, in classification tasks. It can also be useful for other data analysis tasks. The proposed approach is based on the combination of the usage of the text similarity measure and two methods: latent semantic analysis and self-organizing map. First, the text data must be pre-processed by selecting various filters to clean the data from unnecessary and irrelevant information. Latent semantic analysis has been selected to reduce the vectors dimensionality of the obtained vectors that correspond to each text from the analysed data. The cosine similarity distance has been used to determine which of the multi-label text data class should be changed or adjusted. The self-organizing map has been selected as the key method to detect similarity between text data and make decisions for a new class assignment. The experimental investigation has been performed using the newly collected multi-label text data. Financial news data in the Lithuanian language have been collected from four public websites and classified by experts into ten classes manually. Various parameters of the methods have been analysed, and the influence on the final results has been estimated. The final results are validated by experts. The research proved that the proposed approach could be helpful to verify and adjust multi-label text data classes. 82% of the correct assignments are obtained when the data dimensionality is reduced to 40 using the latent semantic analysis, and the self-organizing map size is reduced from 40 to 5 by step 5.

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“…In machine learning applications, the traditional single label classification (SLC) problem has been explored substantially. However, more recently, the multi-label classification (MLC) problem has attracted increasing research interest because of its wide range of applications, such as text classification [ 1 , 2 ], social network analysis [ 3 ], gene function classification [ 4 ], and image/video annotation [ 5 ]. With SLC, one instance only belongs to one category, whereas with MLC, it can be allocated to multiple categories simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification

Wang

Wan

et al. 2020

Entropy

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Multi-label classification (MLC) is a supervised learning problem where an object is naturally associated with multiple concepts because it can be described from various dimensions. How to exploit the resulting label correlations is the key issue in MLC problems. The classifier chain (CC) is a well-known MLC approach that can learn complex coupling relationships between labels. CC suffers from two obvious drawbacks: (1) label ordering is decided at random although it usually has a strong effect on predictive performance; (2) all the labels are inserted into the chain, although some of them may carry irrelevant information that discriminates against the others. In this work, we propose a partial classifier chain method with feature selection (PCC-FS) that exploits the label correlation between label and feature spaces and thus solves the two previously mentioned problems simultaneously. In the PCC-FS algorithm, feature selection is performed by learning the covariance between feature set and label set, thus eliminating the irrelevant features that can diminish classification performance. Couplings in the label set are extracted, and the coupled labels of each label are inserted simultaneously into the chain structure to execute the training and prediction activities. The experimental results from five metrics demonstrate that, in comparison to eight state-of-the-art MLC algorithms, the proposed method is a significant improvement on existing multi-label classification.

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Efficient classification of multi-labeled text streams by clashing

Cited by 24 publications

References 68 publications

An Improved Clustering Method for Detection System of Public Security Events Based on Genetic Algorithm and Semisupervised Learning

An Improved Clustering Method for Detection System of Public Security Events Based on Genetic Algorithm and Semisupervised Learning

Approach for Multi-Label Text Data Class Verification and Adjustment Based on Self-Organizing Map and Latent Semantic Analysis

Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification

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