Step-Factor Resampling Technique for Imbalanced Sequence Data Classification

2020 ◽  
pp. 125-134
Author(s):  
Iroshan Aberathne ◽  
Chamila Walgampaya ◽  
Udara Rathnayake
Keyword(s):  
Sequence Data ◽  
Data Classification ◽  
Step Factor

Length-weighted string kernels for sequence data classification

2007 ◽  
Vol 28 (13) ◽  
pp. 1651-1656 ◽  
Author(s):  
Shengfeng Tian ◽  
Shaomin Mu ◽  
Chuanhuan Yin
Keyword(s):  
Sequence Data ◽  
Data Classification ◽  
String Kernels

Advances in Classification of Sequence Data

2008 ◽  
pp. 143-174 ◽  
Author(s):  
Pradeep Kumar ◽  
P. Radha Krishna ◽  
Raju S. Bapi ◽  
T. M. Padmaja
Keyword(s):  
Data Mining ◽  
Web Mining ◽  
Sequence Data ◽  
Data Classification ◽  
Knowledge Discovery In Databases ◽  
Sequential Data ◽  
Stream Data ◽  
Stream Data Mining ◽  
Discrete Sequence ◽  
Mining Sequence

In recent years, advanced information systems have enabled collection of increasingly large amounts of data that are sequential in nature. To analyze huge amounts of sequential data, the interdisciplinary field of Knowledge Discovery in Databases (KDD) is very useful. The most important step within the process of KDD is data mining, which is concerned with the extraction of the valid patterns. Recent research focus in data mining includes stream data mining, sequence data mining, web mining, text mining, visual mining, multimedia mining and multi-relational data mining. Sequence data may be discrete or continuous in nature. Most of the research on discrete sequence data concentrated on the discovery of frequently occurring patterns. However, comparatively less amount of work has been carried out in the area of discrete sequence data classification. In this chapter, data taxonomy is introduced with a review of the state of art for sequence data classification. The usefulness of embedding partial subsequence information extracted using sliding window technique into traditional classifier like kNN has been demonstrated. kNN has been tested with various vector based distance/similarity metrics. Further, with the use of S3M similarity metric, the full subsequence information embedded in the data sequences is extracted. The experimental data taken is DARPA’98 IDS benchmark dataset collected from UCIML dataset repository. The chapter closes by pointing out various application areas of sequence data and also the open issues in sequence data classification problem.

Diversification of the North American Doellingeria-Eucephalus Clade (Astereae: Asteraceae) Inferred from Molecular and Morphological Evidence

2019 ◽  
Vol 44 (4) ◽  
pp. 930-942
Author(s):  
Geraldine A. Allen ◽  
Luc Brouillet ◽  
John C. Semple ◽  
Heidi J. Guest ◽  
Robert Underhill
Keyword(s):  
North American ◽  
Sequence Data ◽  
Phylogenetic Analyses ◽  
Sister Group ◽  
Morphological Evidence ◽  
South American ◽  
New Combinations ◽  
The North ◽  
Hybridization Event ◽  
Ancient Hybridization

Abstract—Doellingeria and Eucephalus form the earliest-diverging clade of the North American Astereae lineage. Phylogenetic analyses of both nuclear and plastid sequence data show that the Doellingeria-Eucephalus clade consists of two main subclades that differ from current circumscriptions of the two genera. Doellingeria is the sister group to E. elegans, and the Doellingeria + E. elegans subclade in turn is sister to the subclade containing all remaining species of Eucephalus. In the plastid phylogeny, the two subclades are deeply divergent, a pattern that is consistent with an ancient hybridization event involving ancestral species of the Doellingeria-Eucephalus clade and an ancestral taxon of a related North American or South American group. Divergence of the two Doellingeria-Eucephalus subclades may have occurred in association with northward migration from South American ancestors. We combine these two genera under the older of the two names, Doellingeria, and propose 12 new combinations (10 species and two varieties) for all species of Eucephalus.

ANALISIS NILAI KOHESI DAN KOHERENSIDALAM TERJEMAHAN AL-QUR’AN SURAH AL AL ZALZALAH

2016 ◽  
Vol 1 (2) ◽  
pp. 23
Author(s):  
MUNIRAH MUNIRAH ◽  
HUSAIN SYARIFUDDIN
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Data Classification ◽  
Research Data ◽  
Relative Pronoun ◽  
Second Person ◽  
Record Keeping ◽  
Descriptive Research ◽  
Qualitative Descriptive

This study aimed to describe the value of cohesion and coherence contained in the translation of the Qur'an surah Al Zalzalah. This study was a qualitative descriptive research, research data collection techniques using three techniques namely, inventory, rading and understanding, and record keeping. The data analysis used the coding of data, classification data, and the determination of the data. The results showed that the cohesion markers used in the translation of surah Al Zalzalah discourse are: 1) reference, 2) pronouns, ie pronouns second person, and third, the relative pronoun, the pronoun pointer, pen pronouns and pronouns owner, 3 ) conjunctions, namely temporal conjunctions, coordinating conjunctions, subordinating conjunctions, and conjunctions koorelatif, and 4) a causal ellipsis. It mean that there was a coherence in the translation of surah Al Zalzalah discourse are: the addition or addition, pronouns, repetition or repetition, match words or synonyms, in whole or in part, a comparison or ratio of conclusions or results. Keywords: Cohesion, Coherence, sura Al Zalzalah AbstrakPenelitian ini bertujuan untuk mendeskripsikan nilai kohesi dan koherensi yang terdapat dalam terjemahan Al-Qur’an surah Al Zalzalah. Jenis penelitian ini termasuk jenis penelitian deskriptif kualitatif, Teknik pengumpulan data penelitian menggunakan tiga teknik yakni, inventarisasi, baca simak, dan pencatatan. Teknik analisis data menggunakan pengodean data, pengklasifikasian data, dan penentuan data. Hasil penelitian menunjukkan bahwa pemarkah kohesi yang digunakan dalam wacana terjemahan surah Al Zalzalah adalah: 1) referensi, 2) pronomina, yaitu kata ganti orang kedua, dan ketiga, kata ganti penghubung, kata ganti penunjuk, kata ganti penanya dan kata ganti empunya, 3) konjungsi, yaitu konjungsi temporal, konjungsi koordinatif, konjungsi subordinatif, dan konjungsi koorelatif, dan 4) elipsis kausal. Sarana koherensi yang terdapat di dalam wacana terjemahan surah Al Zalzalah adalah: penambahan atau adisi, pronomina, pengulangan atau repetisi, padan kata atau sinonim, keseluruhan atau bagian, komparasi atau perbandingan simpulan atau hasil.Kata Kunci: Kohesi, Koherensi, surah Al Zalzalah

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