Multivariable stream data classification using motifs and their temporal relations

Seo, Sungbo; Kang, Jaewoo; Ryu, Keun Ho

doi:10.1016/j.ins.2009.06.036

Cited by 3 publications

(3 citation statements)

References 37 publications

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“…Seo et al [12] proposed a method for multivariable stream data classification, which is similar to our own. The main idea behind their method is to transform a raw time series into a symbolic sequence by calculating the difference between two consecutive data points, and then assigning the value to a symbol based on its range values so that the n-gram is considered as a word.…”

Section: Related Workmentioning

confidence: 79%

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Textual Approximation Methods for Time Series Classification: TAX and l-TAX

Maruf

Huang

Kawagoe

2014

IEICE Trans. Inf. & Syst.

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SUMMARYA lot of work has been conducted on time series classification and similarity search over the past decades. However, the classification of a time series with high accuracy is still insufficient in applications such as ubiquitous or sensor systems. In this paper, a novel textual approximation of a time series, called TAX, is proposed to achieve high accuracy time series classification. l-TAX, an extended version of TAX that shows promising classification accuracy over TAX and other existing methods, is also proposed. We also provide a comprehensive comparison between TAX and l-TAX, and discuss the benefits of both methods. Both TAX and l-TAX transform a time series into a textual structure using existing document retrieval methods and bioinformatics algorithms. In TAX, a time series is represented as a document like structure, whereas l-TAX used a sequence of textual symbols. This paper provides a comprehensive overview of the textual approximation and techniques used by TAX and l-TAX

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Section: Related Workmentioning

confidence: 79%

“…Although there have been some previous researches [5], [8]- [12], as described in Sect. 2, in which a time series is effectively represented by a set of symbols, they are only for a few specific areas of applications.…”

Section: Time Series Textual Approximationmentioning

confidence: 99%

Textual Approximation Methods for Time Series Classification: TAX and l-TAX

Maruf

Huang

Kawagoe

2014

IEICE Trans. Inf. & Syst.

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“…The paper [4] gave an up-to-data learning algorithm for binary data stream classification by the fuzzy pattern trees. The paper [5] proposed a new classification models for multivariable data streams by different classification models. The paper [6] gave a new class detection model for data streams with concept drafting.…”

Section: Introductionmentioning

confidence: 99%

Cost-sensitive incremental Classification under the MapReduce framework for Mining Imbalanced Massive Data Streams

Huang¹

2015

Journal of Discrete Mathematical Sciences and Cryptography

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Aiming at the limitation of incremental learning for the imbalanced massive data streams, this paper proposes the approach of the cost-sensitive incremental classification under the MapReduce framework for imbalanced massive data streams (CILCIDS). Firstly, this paper gives cost-sensitive concept drift detection for massive data stream under the MapReduce framework by counting the recession numbers of the pure and tolerance clusters. Secondly, we give Cost-sensitive SVM algorithm based on incremental learning. The new incremental samples can be divided into two parts, and only the samples against KKT conditions are used for the incremental learning. At last, the imbalanced massive data streams are divided under the MapReduce framework and are processed in parallel. The cost-sensitive incremental learning classification based on cloud computing platform is developed, and the weighted cost-sensitive ensemble classifier is constructed. The experiments show that the proposed incremental learning algorithm under the MapReduce framework is feasible and correct. CILCIDS has high performance by comparing to the other classification algorithms for imbalanced data streams, and can be effective to deal with unbalanced data stream with concept drift.

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