The Spherical Hidden Markov Self Organizing Map for Learning Time Series Data

Niina, Gen; Dozono, Hiroshi

doi:10.1007/978-3-642-33269-2_71

Cited by 7 publications

(1 citation statement)

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“…HMM and its variants have found a wide variety of applications. There was a hierarchical hidden Markov model (HHMM) for real-time finger motion synthesis [38], a hierarchical multivariate HMM with reactive interpolation functionality for full-body motion reconstruction [39], a combining speaker-specific Gaussian mixture model (GMM) with a syllable-based HMM for speaker recognition [40], and a Spherical-Self Organizing Map (S-SOM) with HMM for classifying sets of time series [41], to list just a few examples. HMMs have also laid a solid foundation for their applications in NLP, including part-of-speech tagging in many languages [42,43] and name entity recognition [44].…”

Section: Hidden Markov Model For Spam Detecionsmentioning

confidence: 99%

A Discrete Hidden Markov Model for SMS Spam Detection

Xia

Chen

2020

Applied Sciences

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Many machine learning methods have been applied for short messaging service (SMS) spam detection, including traditional methods such as naïve Bayes (NB), vector space model (VSM), and support vector machine (SVM), and novel methods such as long short-term memory (LSTM) and the convolutional neural network (CNN). These methods are based on the well-known bag of words (BoW) model, which assumes documents are unordered collection of words. This assumption overlooks an important piece of information, i.e., word order. Moreover, the term frequency, which counts the number of occurrences of each word in SMS, is unable to distinguish the importance of words, due to the length limitation of SMS. This paper proposes a new method based on the discrete hidden Markov model (HMM) to use the word order information and to solve the low term frequency issue in SMS spam detection. The popularly adopted SMS spam dataset from the UCI machine learning repository is used for performance analysis of the proposed HMM method. The overall performance is compatible with deep learning by employing CNN and LSTM models. A Chinese SMS spam dataset with 2000 messages is used for further performance evaluation. Experiments show that the proposed HMM method is not language-sensitive and can identify spam with high accuracy on both datasets.

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