A new semantic-based feature selection method for spam filtering

Méndez, J.; Cotos-Yáñez, Tomás R.; Ruano-Ordás, David

doi:10.1016/j.asoc.2018.12.008

Cited by 69 publications

(58 citation statements)

References 45 publications

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“…proposed [31] a feature selection method based on semantic ontology cluster to build feature vectors, the words are grouped into topics for training different Machine Learning tasks.…”

Section: Feature Selection Based Methodsmentioning

confidence: 99%

A Hybrid Classification Method via Character Embedding in Chinese Short Text With Few Words

Zhu

Yue

et al. 2020

IEEE Access

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Last decades have witnessed the significance development of research in short text classification. However, most existing methods only focus on the text which contained dozens of words like Twitter or MicroBlog, but not take the short text with few words like news headline or invoice name into consideration. Meanwhile, contemporary short text classification methods either to expand feature of short text with external corpus or to learn the feature representation from all the texts, which have not take the difference between words of short text into full consideration. Notably, the classification of short text with few words are usually determined by a few specific key words contrary to documents classification or traditional short text classification. To address these problems, this paper propose a hybrid classification method of Attention mechanism and Feature selection via Character embedding in Chinese short text with few words, called AFC. More specifically, firstly, the character embedding is computed to represent Chinese short texts with few words, which takes full advantage of short text information without external corpus. Secondly, attention-based LSTM is introduced in our method to project the data into feature representation space with weighting, which make the keywords in classification have more subtle value. Furthermore, the semantic similarity between content and class label information is calculated for feature selection, which reduces the possible negative influence of some redundant information on classification. Experiments on real-world datasets demonstrate the effectiveness of our method compared to other competing methods.

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“…proposed [31] a feature selection method based on semantic ontology cluster to build feature vectors, the words are grouped into topics for training different Machine Learning tasks.…”

Section: Feature Selection Based Methodsmentioning

confidence: 99%

A Hybrid Classification Method via Character Embedding in Chinese Short Text With Few Words

Zhu

Yue

et al. 2020

IEEE Access

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show abstract

“…In spite of the fact that they are removed from words, point highlights represent the topic of the rather than crude terms. One of the significant downsides of maintaining a strategic distance from FS lies in the repetition of highlights [5].…”

Section: Semantic Based Approachmentioning

confidence: 99%

Spam, a Digital Pollution and Ways to Eradicate It

Reddy,

Chaudhary,

Kandasamy

2019

IJEAT

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Due to the growing popularity of the microblogging and networking sites like twitter, Gmail, Facebook etc., there has been an increase in the number of spammers. Spammers on Twitter seem to be more dangerous than the mail spammers as they exploit the limitation on the characters of Twitter for their own purposes. Spammers have also become creative in framing their content to cleverly escape the classifiers. This survey is thus mainly used to discuss and analyze the recent research that had been put forth regarding the spam detection in social media sites such as Twitter. This survey analyses the papers that tackled various problems faced on Twitter and the problems faced by the methods that have already been presented before. We then compared all the methods present in the papers to see which method or combination of methods could give the best result in detecting spam.

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“…ods do not consider any learning algorithm and select features based on intrinsic properties of data, such as relevance, information gain, and distance [6], [7]. Due to the advantage of simplicity and interpretability, filter models have been widely studied [8].…”

Section: Introductionmentioning

confidence: 99%

Multilabel Feature Selection Using Relief and Minimum Redundancy Maximum Relevance Based on Neighborhood Rough Sets

Miao-miao

Sun

et al. 2020

IEEE Access

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Recently, multilabel classification is of increasing interest in machine learning and artificial intelligence. However, the distances of samples in most Relief methods easily result in heterogeneous or similar samples abnormal when the distances are very large. Besides, the classification margin as a neighborhood radius for some reduction algorithms may be meaningless when the margin is too large. To overcome these drawbacks, this paper presents a multilabel feature selection method using the improved Relief and minimum redundancy maximum relevance (MRMR) based on neighborhood rough sets. First, the number of heterogeneous and similar samples is introduced to improve the label weighting method which can eliminate the influence of the large distances of samples. By combining with the new label weighting, the distances between the sample and its nearest-neighbor heterogeneous sample and between the sample and its nearest-neighbor similar sample are presented to develop a new feature weighting method. Second, the number of heterogeneous and similar samples continues to be used to improve the classification margin, thereby constraining the neighborhood radius, based on which the neighborhood approximation accuracy is constructed to effectively measure the uncertainty of samples in the boundary region and the completeness of knowledge. Third, by integrating with the new neighborhood approximation accuracy, two types of mutual information between features and labels and among features are proposed, and then the mutual informationbased MRMR model is investigated to evaluate the significance of features. Finally, a multilabel feature selection algorithm is designed for improving the classification performance of multilabel data. Experimental results on thirteen public datasets illustrate the effectiveness of our developed algorithm that can select the significant features and achieve great performance for multilabel datasets.

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A new semantic-based feature selection method for spam filtering

Cited by 69 publications

References 45 publications

A Hybrid Classification Method via Character Embedding in Chinese Short Text With Few Words

A Hybrid Classification Method via Character Embedding in Chinese Short Text With Few Words

Spam, a Digital Pollution and Ways to Eradicate It

Multilabel Feature Selection Using Relief and Minimum Redundancy Maximum Relevance Based on Neighborhood Rough Sets

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