Yarı-gözetimli veri sınıflandırma için kolektif bir öğrenme yaklaşımı

Year 2018, Volume: 24 Issue: 5, 864 - 869, 12.10.2018

Nur Uylaş Satı

Abstract

Yarı-gözetimli veri
sınıflandırma, makine öğrenme ve veri madenciliğinde önemli bir çalışma
alanıdır çünkü az sayıda etiketli ve çok sayıda etiketsiz veri içeren veri
kümeleri ile ilgilenmektedir. Gerçek hayat veri kümelerinin çoğu bu özelliği
taşıdığından birçok araştırmacı bu alana ilgi duymaktadır. Bu makalede
yarı-gözetimli veri sınıflandırma problemlerinin çözümü için kolektif bir
yöntem önerilmiştir. Konuyu daha iyi anlamak için R¹ de tanımlı veri
kümeleri oluşturup önerilen algoritmalar bu veri kümelerine uygulanmıştır.
Gelişkin tekniklerle karşılaştırma yapmak için en iyi bilinen WEKA makine
öğrenme programı kullanılmıştır. Çalışmalar UCI veri kümesi deposunda bulunan
gerçek hayat veri kümeleri üzerinde uygulanmıştır. 10 katlı çapraz geçerlilik
ölçütü kullanılarak elde edilen değerlendirme sonuçları tablolarda sunulmuştur.

Keywords

Yarı-gözetimli veri sınıflandırma, Kümeleme yöntemi, Gözetimli veri sınıflandırma, Makine öğrenme, Matematiksel programlama

A collective learning approach for semi-supervised data classification

Abstract

Semi-supervised
data classification is one of significant field of study in machine learning
and data mining since it deals with datasets which consists both a few labeled
and many unlabeled data. The researchers have interest in this field because in
real life most of the datasets have this feature. In this paper we suggest a
collective method for solving semi-supervised data classification problems.
Examples in R¹ presented and solved to gain a clear understanding.
For comparison between state of art methods, well-known machine learning tool
WEKA is used. Experiments are made on real-world datasets provided in UCI
dataset repository. Results are shown in tables in terms of testing accuracies
by use of ten fold cross validation.

Keywords

Semi- Supervised data classification, Clustering method, Supervised data classification, Machine learning, Mathematical programming

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