Performance Evaluation of Classification Algorithms in Data Mining and an Application With the R Language

Çınar, Ayşe

doi:10.14783/maruoneri.vi.522168

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…But when calculating entropy on properties, it is considered in the target. In this case, entropy is defined as follows: (7) Information gain (Gain) is based on subtracting all entropy after dividing a dataset on a feature as follows). If the entropy is small, the importance of the feature increases for the Decision Tree algorithm ID3.…”

Section: The Steps Of the Decision Tree Algorithmmentioning

confidence: 99%

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An Application of Data Mining Problem by Python: A Case Study on Fruit Classification

Akpolat,

Ertürk

2023

Rec. Agric. Food. Chem

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Fruits contain vitamins and dietary fibers, a vital source of the human diet. According to fruit production statistics, millions of metric tons of fruits was produced worldwide, and the advanced agricultural fruit recognition system with a simple camera or sensor will play an excellent role for farmers and general people. For the classification of fruits and vegetables, the features that come to mind first are sizing, color, and smell. The other ones are physical, chemical, and biological properties, and their tastes are important for identification, too. Such features also provide a perfect environment for creating imbalanced and incomplete datasets under the open-set protocol. The traditional methods that include a physical inspection of each fruit are less efficient, resulting in the development of more efficient and effective classification algorithms. Data mining algorithms for classification is operated in some software without being directly coded, KNIME, or can be modeled in code-able software such as PYTHON. It is a widely used high-level, general-purpose, interpreted, dynamic programming language, and pandas, numpy, matplotlib, and scikit-learn packages is frequently used as the basis for programming with PYTHON. This work aims to distinguish between different types of fruits using a simple dataset for the task of training a classifier. In this study prepared using the data, 59 fruit samples with the species name, subspecies name, weight, width, height, and color properties were analyzed using Decision Tree (DTR), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Gaussian Navie Bayes (GNB) and Support Vector Machine (SVM) classifiers. Accuracy values for each classifier were defined and compared with each other, and then the classifier with the highest accuracy was examined in detail. In the decision tree of samples classified according to fruit names, it is understood that height is the most effective measure in classifying fruits, and they are in the Bin 1 class (15 pieces). The second effective one is in the Bin 1 class (15 units) with height. The third effective one is in the Bin 1 class with width (12 peaces).

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Section: The Steps Of the Decision Tree Algorithmmentioning

confidence: 99%

“…The language provides constructs intended to enable clear programs at both small and large scales. Pandas, numpy, matplotlib, and scikit-learn packages are frequently used as the basis for programming with PYTHON [5,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

An Application of Data Mining Problem by Python: A Case Study on Fruit Classification

Akpolat,

Ertürk

2023

Rec. Agric. Food. Chem

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“…Örüntü kavramı veri içerisinde gözlemlenebilir, ölçülebilir ve tekrar edilebilir bir bilgidir. Sınıflandırma algoritmaları ulaşılmak istenen bilgiyi hedefleyerek, veriyi ortak özelliklerine göre belirli gruplara (sınıflara) ayırmaktadır [10,11].…”

Section: Sınıflandırma Modeliunclassified

Makine Öğrenmesi Teknikleriyle Diyabet Hastalığının Sınıflandırılması

Başer¹,

Yangin²,

Saridaş³

2021

Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Özet: Diyabet, dünya çapında artan ve gerçekleşen ölümlerin önde gelen nedenlerinden biridir. Sürekli artan vaka sayısı diyabetin önlenmesi, erken teşhisi, tedavisi ve takibi konularında bilimsel çalışmalara ihtiyaç duyulduğunu göstermektedir. Son dönemlerde medikal alanda yaşanan teknolojik gelişmeler sayesinde elde edilen verinin analiz edilmesi, hastalıkların tanı ve tedavi sürecine olumlu katkılar yapmaktadır. Diyabet hastalığı kapsamında da araştırmacılar, hastalığın teşhis edilmesine yönelik, veriye dayalı sistematik yaklaşımlar geliştirmeye çalışmaktadırlar. Bu amaç doğrultusunda çalışmada, 1999-2008 yılları arasında ABD'de bulunan 130 hastanedeki 70000 kayda ait sağlık vakalarından elde edilmiş veri seti düzenlenerek, bireylerin diyabet durumuna göre sınıflandırılması hedeflenmiştir. Sınıflandırma için veri setine uygun makine öğrenmesi algoritmalarından yararlanılmış ve bu algoritmaların sonuçları performans ölçütlerine göre karşılaştırılmıştır. Elde edilen sonuçlara göre, en iyi performans gösteren beş sınıflandırma algoritması (Karar ağaçları, k-en yakın komşuluk, Lojistik regresyon, Naive Bayes ve Rastgele orman) değerlendirmeye alınmış olup en iyi doğru sınıflandırma performansı Rastgele orman algoritması ile elde edilmiştir.

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“…One of the most important problems of the text classification [21] is that the texts to be classified are not structural [22]. The success rate can be increased by April, 2021 using deep learning method in analysis studies conducted with classification algorithms [23] on text processing [24,25]. As a success criterion, the quality of word representations comes to the fore.…”

Section: Introductionmentioning

confidence: 99%

Analysis of whether news on the Internet is real or fake by using deep learning methods and the TF-IDF algorithm

Korkmaz

Aydın

Yılmaz

2021

International Advanced Researches and Engineering Journal

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Internet use has become increasingly widespread nowadays. In addition, there is a significant increase in the amount of text content produced in digital media. However, the accuracy and inaccuracy of the news we read and the content produced in a large number are also unknown. In this study, classification and analysis of whether the news is real or not were done by using Deep Learning methods. For the English news, the data set created by Katharine Jarmul was used. The data set contained a total of 6336 news items. The distribution of this data set, which consisted of political and political news, was 50% fake and 50% real. The method used in text classification was Term Frequency -Inverse Document Frequency (TF-IDF). The classification was made with the data set used and 93.88% success and 6.12% error were obtained as a result of the analysis.

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Performance Evaluation of Classification Algorithms in Data Mining and an Application With the R Language

Cited by 4 publications

References 5 publications

An Application of Data Mining Problem by Python: A Case Study on Fruit Classification

An Application of Data Mining Problem by Python: A Case Study on Fruit Classification

Makine Öğrenmesi Teknikleriyle Diyabet Hastalığının Sınıflandırılması

Analysis of whether news on the Internet is real or fake by using deep learning methods and the TF-IDF algorithm

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