Spam Email Classification using an Adaptive Ontology

Youn, Seongwook; McLeod, Dennis

doi:10.4304/jsw.2.3.43-55

Cited by 20 publications

(11 citation statements)

References 44 publications

(25 reference statements)

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“…In its simplest form, text classification is binary in nature, such as the categorization of spam vs. non-spam email (Youn and McLeod, 2007). Researchers from distinct fields are exposed to a wide range of text classification problems.…”

Section: Introductionmentioning

confidence: 99%

HLP$@$UPenn at SemEval-2017 Task 4A: A simple, self-optimizing text classification system combining dense and sparse vectors

Sarker¹,

González²

2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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We present a simple supervised text classification system that combines sparse and dense vector representations of words, and the generalized representations of words via clusters. The sparse vectors are generated from word n-gram sequences (1-3). The dense vector representations of words (embeddings) are learned by training a neural network to predict neighboring words in a large unlabeled dataset. To classify a text segment, the different vector representations of it are concatenated, and the classification is performed using Support Vector Machines (SVMs). Our system is particularly intended for use by nonexperts of natural language processing and machine learning, and, therefore, the system does not require any manual tuning of parameters or weights. Given a training set, the system automatically generates the training vectors, optimizes the relevant hyper-parameters for the SVM classifier, and trains the classification model. We evaluated this system on the SemEval-2017 English sentiment analysis task. In terms of average F1-Score, our system obtained 8 th position out of 39 submissions (F1-Score: 0.632, average recall: 0.637, accuracy: 0.646).

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Section: Introductionmentioning

confidence: 99%

HLP$@$UPenn at SemEval-2017 Task 4A: A simple, self-optimizing text classification system combining dense and sparse vectors

Sarker¹,

González²

2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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“…• high volume: one has to deal with huge amount of existing training data, in million or even billion scale; • high velocity: new data often arrives sequentially and very rapidly, e.g., about 182.9 billion emails are sent/received worldwide every day according to an email statistic report by the Radicati Group [1]; • high dimensionality: there are a large number of features, e.g., for some spam email classification tasks, the length of the vocabulary list can go up from 10, 000 to 50, 000 or even to million scale; • high sparsity: many feature elements are zero, and the faction of active features is often small, e.g., the spam email classification study in [2] showed that accuracy saturates with dozens of features out of tens of thousands of features. The above characteristics present huge challenges for big data stream classification tasks when using conventional data stream classification techniques that are often restricted to batch learning setting.…”

Section: Introductionmentioning

confidence: 99%

High-Dimensional Data Stream Classification via Sparse Online Learning

Wang

Zhao

et al. 2014

2014 IEEE International Conference on Data Mining

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Abstract-The amount of data in our society has been exploding in the era of big data today. In this paper, we address several open challenges of big data stream classification, including high volume, high velocity, high dimensionality, and high sparsity. Many existing studies in data mining literature solve data stream classification tasks in a batch learning setting, which suffers from poor efficiency and scalability when dealing with big data. To overcome the limitations, this paper investigates an online learning framework for big data stream classification tasks. Unlike some existing online data stream classification techniques that are often based on firstorder online learning, we propose a framework of Sparse Online Classification (SOC) for data stream classification, which includes some state-of-the-art first-order sparse online learning algorithms as special cases and allows us to derive a new effective second-order online learning algorithm for data stream classification. We conduct an extensive set of experiments, in which encouraging results validate the efficacy of the proposed algorithms in comparison to a family of stateof-the-art techniques on a variety of data stream classification tasks.

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“…Among the supervised approaches, we can mention the use of rule-based systems [Cohen, 1996], SupportVector Machines [Drucker et al, 1999, Kiritchenko and Matwin, 2011, Yoo et al, 2011, Bayesian networks [Sahami et al, 1998, Androutsopoulos et al, 2000, Sakkis et al, 2001, Isozaki et al, 2005, memory-based reasoning [Segal andKephart, 1999, Delany et al, 2005], decision trees [Youn and McLeod, 2007], linear logistic regression [Aberdeen et al, 2010], neural networks [Yu and Zhu, 2009] and semantic analysis methods [Park and An, 2010].…”

Section: Related Workmentioning

confidence: 99%