Pattern Based Sequence Classification

Zhou, Cheng; Čule, Boris; Goethals, Bart

doi:10.1109/tkde.2015.2510010

Cited by 53 publications

(48 citation statements)

References 32 publications

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“…Document classification, another instance of the sequence classification problem, is performed by using the order of words as a major feature [25], a sentimental analysis is performed by using the sentiment lexicons of words [26], and the pattern-based sequence classification is studied in more general areas [27]. In the study by [28], the class classification probability of sentences, which is a kind of sequence, was expressed with the following equations using the class probability of patterns composing a sentence:

c l a s s o f a s e q u e n c e = a r g \max_{c} p (c | s e q u e n c e),

p (c | s e q u e n c e) = \frac{{100%true \sum}_{p a t t e r n}^{} (O \cdot S \cdot p (c | p a t t e r n))}{n},

p (c | p a t t e r n) = \frac{L}{G},

where a pattern is a set of words in the sentence of arbitrary length.…”

Section: Encoding-based Wave2vec Time Series Classifiermentioning

confidence: 99%

Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease

Kim

2018

IJERPH

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Interest in research involving health-medical information analysis based on artificial intelligence, especially for deep learning techniques, has recently been increasing. Most of the research in this field has been focused on searching for new knowledge for predicting and diagnosing disease by revealing the relation between disease and various information features of data. These features are extracted by analyzing various clinical pathology data, such as EHR (electronic health records), and academic literature using the techniques of data analysis, natural language processing, etc. However, still needed are more research and interest in applying the latest advanced artificial intelligence-based data analysis technique to bio-signal data, which are continuous physiological records, such as EEG (electroencephalography) and ECG (electrocardiogram). Unlike the other types of data, applying deep learning to bio-signal data, which is in the form of time series of real numbers, has many issues that need to be resolved in preprocessing, learning, and analysis. Such issues include leaving feature selection, learning parts that are black boxes, difficulties in recognizing and identifying effective features, high computational complexities, etc. In this paper, to solve these issues, we provide an encoding-based Wave2vec time series classifier model, which combines signal-processing and deep learning-based natural language processing techniques. To demonstrate its advantages, we provide the results of three experiments conducted with EEG data of the University of California Irvine, which are a real-world benchmark bio-signal dataset. After converting the bio-signals (in the form of waves), which are a real number time series, into a sequence of symbols or a sequence of wavelet patterns that are converted into symbols, through encoding, the proposed model vectorizes the symbols by learning the sequence using deep learning-based natural language processing. The models of each class can be constructed through learning from the vectorized wavelet patterns and training data. The implemented models can be used for prediction and diagnosis of diseases by classifying the new data. The proposed method enhanced data readability and intuition of feature selection and learning processes by converting the time series of real number data into sequences of symbols. In addition, it facilitates intuitive and easy recognition, and identification of influential patterns. Furthermore, real-time large-capacity data analysis is facilitated, which is essential in the development of real-time analysis diagnosis systems, by drastically reducing the complexity of calculation without deterioration of analysis performance by data simplification through the encoding process.

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c l a s s o f a s e q u e n c e = a r g \max_{c} p (c | s e q u e n c e),

p (c | s e q u e n c e) = \frac{{100%true \sum}_{p a t t e r n}^{} (O \cdot S \cdot p (c | p a t t e r n))}{n},

p (c | p a t t e r n) = \frac{L}{G},

where a pattern is a set of words in the sentence of arbitrary length.…”

Section: Encoding-based Wave2vec Time Series Classifiermentioning

confidence: 99%

Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease

Kim

2018

IJERPH

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show abstract

“…Many sequence classification techniques have been proposed [15,16,17], each focusing on a different approach ranging from extensions to sequence pattern mining algorithms, to statistical approaches that infer the explanatory power of subsequences. They can be classified as either being direct, i.e., the features are extracted according to their strength towards the classifier, or indirect, i.e., all features are generated and later selected by a classifier.…”

Section: Sequence Classificationmentioning

confidence: 99%

“…They can be classified as either being direct, i.e., the features are extracted according to their strength towards the classifier, or indirect, i.e., all features are generated and later selected by a classifier. [15] extends the SPADE algorithm with an interestingness measure that is based on both the support and the window (cohesion) in which the items of the constraint occur. In [16] BIDE-D(C) is introduced which rather incorporates information gain into BIDE to provide a direct sequence classification approach.…”

Section: Sequence Classificationmentioning

confidence: 99%

“…By fixing the pattern base, it becomes easy to write a specific and fast algorithm for retrieving them from large databases. The technique employs only binary constraints, however, other studies such as [15] have already revealed that for sequence classification, the length of the patterns does not have to exceed 3, or even 2.…”

Section: Sequence Classificationmentioning

confidence: 99%

“…The datasets that were used are summarized in Table 4 and are a mix with both a large set of distinct items, as well as a large number of data entries. They are discussed in more detail in [17] and [15]. All techniques were first employed to generate interesting sequences, and next to build a predictive model by using the presence of the sequences as a binary feature.…”

Section: Data and Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Behavioral Constraint Template-Based Sequence Classification

Smedt

Deeva

Weerdt

2017

Machine Learning and Knowledge Discovery in Databases

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In this paper we present the interesting Behavioral Constraint Miner (iBCM), a new approach towards classifying sequences. The prevalence of sequential data, i.e., a collection of ordered items such as text, website navigation patterns, traffic management, and so on, has incited a surge in research interest towards sequence classification. Existing approaches mainly focus on retrieving sequences of itemsets and checking their presence in labeled data streams to obtain a classifier. The proposed iBCM approach, rather than focusing on plain sequences, is template-based and draws its inspiration from behavioral patterns used for software verification. These patterns have a broad range of characteristics and go beyond the typical sequence mining representation, allowing for a more precise and concise way of capturing sequential information in a database. Furthermore, it is possible to also mine for negative information, i.e., sequences that do not occur. The technique is benchmarked against other state-of-the-art approaches and exhibits a strong potential towards sequence classification.

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Multivariate temporal data analysis ‐ a review

Moskovitch

2021

WIREs Data Min & Knowl

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The information technology revolution, especially with the adoption of the Internet of Things, longitudinal data in many domains become more available and accessible for secondary analysis. Such data provide meaningful opportunities to understand process in many domains along time, but also challenges. A main challenge is the heterogeneity of the temporal variables due to the different types of data, whether a measurement or an event, and type of samplings: fixed or irregular. Other variables can be also events that may or not have duration. In this review, we discuss the various types of temporal data, and the various relevant analysis methods. Starting with fixed frequency variables, with forecasting and time series methods, and proceeding with sequential data, and sequential patterns mining, and time intervals mining for events having various time duration. Also the use of various deep learning based architectures for temporal data is discussed. The challenge of heterogeneous multivariate temporal data analysis and discuss various options to deal with it, focusing on an increasingly used option of transforming the data into symbolic time intervals through temporal abstraction and the use of time intervals related patterns discovery for temporal knowledge discovery, clustering, classification prediction, and more. Finally, we discuss the overview of the field, and areas in which more studies and contributions are needed. This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mininganalytics, temporal data mining, time series | INTRODUCTIONWith the introduction of the internet, an exponential growth in text based data, which required new methods to analyze text, retrieve, understand, classify, and more. However, with the recent growing adoption of the Internet of Things, in which devices in various domains become connected to the internet network and produce multitude of logged longitudinal data, we are facing a similar increase in multivariate temporal data that is generated by various devices and sensors. Such data can be logged from smart watches, cars, smart refrigerators, smart phones, televisions, sensors in the space of rooms for various purposes, and many more, which can be useful in various domains, and for different tasks. This trend is expected to increase significantly and requires new methods and more scalability.An extension of temporal data analytics, which are out of the scope of this article, is spatio-temporal data analysis (Gong

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Pattern Based Sequence Classification

Cited by 53 publications

References 32 publications

Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease

Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease

Behavioral Constraint Template-Based Sequence Classification

Multivariate temporal data analysis ‐ a review

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