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CS-IBC: Cuckoo Search Based Incremental Binary Classifier For Data Streams

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: Abdualrhman, Mohammed Ahmed Ali (Author)
مؤلفين آخرين: Padma, M. C. (Co-Author)
المجلد/العدد: مج31, ع3
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2019
الصفحات: 367 - 377
DOI: 10.33948/0584-031-003-009
ISSN: 1319-1578
رقم MD: 974701
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
كلمات المؤلف المفتاحية:
Binary Classification | Data Streams | Incremental Learning | Evolving Systems | Cuckoo Search | Data Driven Model Design | Similarit
رابط المحتوى:
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024 |3 10.33948/0584-031-003-009 
041 |a eng 
044 |b السعودية 
100 |9 525876  |a Abdualrhman, Mohammed Ahmed Ali  |e Author 
245 |a CS-IBC: Cuckoo Search Based Incremental Binary Classifier For Data Streams 
260 |b جامعة الملك سعود  |c 2019 
300 |a 367 - 377 
336 |a بحوث ومقالات  |b Article 
520 |b The act of classifying data streams is widely studied in the literature over the last decade. Incremental or progressive learning strategies are adapted to classify the data streams by many research contributions in recent literature. The contemporary affirmation of recent literature indicate that issues like timeliness, linearity of computational complexity, incremental update of the classifier, and concept drift adaptation in data stream classification are still significant constraints. And there is a need for an algorithm to provide good classification performance with a reasonable response time and maximal classification accuracy. In order to arrive at this, Cuckoo Search Based Incremental Binary Classifier (CS-IBC) has been devised in this manuscript. The contributions of the CS-IBC is to define class labels from training data and fasten the class search through bio inspired strategy called ‘‘CUCKOO Search”. A periodical update of the classifier is also proposed to update the classifier if a set of new labelled records are given. The CS-IBC is tested on KDDCUP data that contains records, which are labelled as attack prone or normal. Metrics such as classification error rate, latency of the classification strategy and classification accuracy deterioration were assessed to estimate the scope of the CS-IBC as binary classifier. The experimental study indicates that the proposed CS-IBC is robust and scalable. ©2017 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc nd/4.0/). 
653 |a علوم الحاسوب  |a الخوارزميات  |a قواعد البيانات 
692 |b Binary Classification  |b Data Streams  |b Incremental Learning  |b Evolving Systems  |b Cuckoo Search  |b Data Driven Model Design  |b Similarit 
700 |9 525878  |a Padma, M. C.  |e Co-Author 
773 |c 009  |e Journal of King Saud University (Computer and Information Sciences)  |f Maǧalaẗ ǧamʼaẗ al-malīk Saud : ùlm al-ḥasib wa al-maʼlumat  |l 003  |m مج31, ع3  |o 0584  |s مجلة جامعة الملك سعود - علوم الحاسب والمعلومات  |v 031  |x 1319-1578 
856 |u 0584-031-003-009.pdf 
930 |d y  |p y 
995 |a science 
999 |c 974701  |d 974701 

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