A study on classification techniques in data mining

Kesavaraj, G.; Sukumaran, S.

doi:10.1109/icccnt.2013.6726842

Cited by 197 publications

(107 citation statements)

References 8 publications

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“…It is a representation method which is used to arrange rules and on meeting different rules the data ends up at different nodes [6]. The basic idea of decision trees is to understand the dataset and take right decision.…”

Section: Decision Treementioning

confidence: 99%

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The Analysis and Impact of Co2 Worldwide

Singh¹,

Ataullah

2017

IJARCSSE

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Abstract-Carbon plays important role in the environment for climate change. Carbon cycle is essential to life on earth but it is recognized as one of the most dominating gas among greenhouse gases Countries emit carbon from various sources which is necessary development. Each year a rank given to each country based on human development index which does not include carbon emissions. This paper uses classification algorithms to categorize countries to predicting the level of development and to find the best performing algorithm.

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Section: Decision Treementioning

confidence: 99%

“…It is a collection of tree indicators called forest [6]. Random Forests develops numerous classification trees.…”

Section: Random Forestmentioning

confidence: 99%

The Analysis and Impact of Co2 Worldwide

Singh¹,

Ataullah

2017

IJARCSSE

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“…It is also widely used in sentiment classification [8]. SVM can be used to learn radial basis function (RBF), polynomial and multi-layer perceptron (MLP) classifiers [7]. …”

Section: Support Vector Machinementioning

confidence: 99%

“…Among current classifier algorithms, RF has excellent accuracy. For estimating missing data, RF has an effective method and when a large proportion of the data are missing, it maintains accuracy [5,7].…”

Section: Random Forestmentioning

confidence: 99%

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Classification Techniques: A Review

Kaur¹,

Verma²

2017

IOSR JCE

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The comparison of machine learning classification algorithms used to diagnose liver cirrhosis disease and a brief review

Güneş

Kasap

Zorlu

2023

Concurrency and Computation

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Liver cirrhosis disease is an important cause of death worldwide. Therefore, early diagnosis of the disease is very important. Machine learning algorithms are frequently used due to its high performance in the field of health, as in many areas. In this study, Multilayer Perceptron-Artificial Neural Networks, Decision Trees, Random Forest, Naïve Bayes, Support Vector Machines, K-Nearest Neighborhood, and Logistic Regression classification algorithms are used to classify the factors affecting liver cirrhosis. The performances of these algorithms are compared according to the accuracy rate, F measure, sensitivity, specificity and Kappa score on real data obtained from 2000 liver cirrhosis patients, and the factors affecting the disease are classified with the most appropriate algorithm. In addition, more than 50 articles covering both liver disease and classification methods are reviewed and the latest developments are presented in the study.

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A study on classification techniques in data mining

Cited by 197 publications

References 8 publications

The Analysis and Impact of Co2 Worldwide

The Analysis and Impact of Co2 Worldwide

Classification Techniques: A Review

The comparison of machine learning classification algorithms used to diagnose liver cirrhosis disease and a brief review

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