Classification of Down Syndrome of Mice Protein Dataset on MongoDB Database

Yıl 2018, Cilt: 6 Sayı: 2, 112 - 117, 30.04.2018

Fahriye Gemci Furat Turgay Ibrikci

https://doi.org/10.17694/bajece.419553

Cited By: 1

Öz

There are samples both with Down Syndrome and without
in mice protein expression data set. It is important to define the reason of
Down Syndrome treatment by means of mice protein for the same treatment seem
human being. In the present study, mice protein expression data set from UCI
repository are classified using Bayesian Network algorithm, K- Nearest
Neighbor, Decision Table, Random Forest and Support Vector Machine which are
some of classification methods.  The classification
algorithms with 10-fold cross validation and by splitting equally as test and
train data are tested to classify on the mice protein data set. The
classification of the data set was succeeded with 94.3519% accuracy in 0.06
seconds using Bayesian
Network, with 99.2593% accuracy in 0.01 seconds using KNN, with 95.4630 % accuracy in 1.2 seconds using
Decision Table, with 100% accuracy in 0.58 seconds using Random Forest and with 100% accuracy in 1.17 seconds using SVM, with 10-fold cross validation. On the other hand, the
classification of the data set was succeeded with 95.3704% accuracy in 0.22
seconds using Bayesian
Network, with 98.3333% accuracy in 0 seconds using KNN, with 98.3333% accuracy in 0.72 seconds using
Decision Table, with 100% accuracy in 0.77 seconds using Random Forest and with 100% accuracy in 1.48 seconds using SVM, by equally train-test data partition.

Anahtar Kelimeler

Bayesian Network, KNN, Decision Table, Random Forest, SVM, Classification, MongoDB, NoSQL

Kaynakça

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