Application of Decision Trees for Classifying Astronomical Objects

Franco-Arcega, Anilu; Flores-Flores, L. G.; Gabbasov, Ruslan

doi:10.1109/micai.2013.29

Cited by 4 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AUROC curve was also used for evaluation of different techniques [18,27] in biomedical data mining. There are 50% of cervical cancer identification in females age (35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53)(54) and around 20% diagnosed more than 65 years old as well as around 15% of between the age of (20 -30). Median age for diagnosis in cervical cancer is 48 years.…”

Section: Resultsmentioning

confidence: 99%

“…Classification algorithms have a wide range of applications which used decision trees other than biomedical domain. Astronomical objects detection [50], fraud detection in banking [51] and financial failure prediction [52] were also utilized decision trees for classification. There were several classification algorithms presented in literature but decision trees were generally utilized because of its simplicity of implementation and ease to understand as compared to other classification methods.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Cervical Cancer Prediction through Different Screening Methods using Data Mining

Alam¹,

Milhan²,

Atif³

et al. 2019

ijacsa

View full text Add to dashboard Cite

Cervical cancer remains an important reason of deaths worldwide because effective access to cervical screening methods is a big challenge. Data mining techniques including decision tree algorithms are used in biomedical research for predictive analysis. The imbalanced dataset was obtained from the dataset archive belongs to the University of California, Irvine. Synthetic Minority Oversampling Technique (SMOTE) has been used to balance the dataset in which the number of instances has increased. The dataset consists of patient age, number of pregnancies, contraceptives usage, smoking patterns and chronological records of sexually transmitted diseases (STDs). Microsoft azure machine learning tool was used for simulation of results. This paper mainly focuses on cervical cancer prediction through different screening methods using data mining techniques like Boosted decision tree, decision forest and decision jungle algorithms as well performance evaluation has done on the basis of AUROC (Area under Receiver operating characteristic) curve, accuracy, specificity and sensitivity. 10-fold cross-validation method was utilized to authenticate the results and Boosted decision tree has given the best results. Boosted decision tree provided very high prediction with 0.978 on AUROC curve while Hinslemann screening method has used. The results obtained by other classifiers were significantly worse than boosted decision tree.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Cervical Cancer Prediction through Different Screening Methods using Data Mining

Alam¹,

Milhan²,

Atif³

et al. 2019

ijacsa

View full text Add to dashboard Cite

show abstract

“…Tree algorithms have been extensively used for classification of various tasks in diverse domains. It include characterizing smoking patterns of older adults 3 ,analyzing students' achievements in distance-learning mode to improve online teaching 4 , identifying core factors of production control in agricultural investment decisions 5 ,understanding behavioral patterns of different kinds of astronomical objects 6 , analyzing financial data 7 , text classification 8 and many more. In the domain of music, there are various applications of tree classifiers which consider classification of various instruments 9 and speech/music classification and segmentation 10 .…”

Section: Introductionmentioning

confidence: 99%

Tree-Based Classification of Tabla Strokes

Deolekar¹,

Abraham²

2018

Current Science

View full text Add to dashboard Cite

The paper attempts to validate the effectiveness of tree classifiers to classify tabla strokes especially the ones which are overlapping in nature. It uses decision tree, ID3 and random forest as classifiers. A custom made data sets of 650 samples of 13 different tabla strokes were used for experimental purpose. 31 different features with their mean and variances were extracted for classification. Three data sets consisting of 21361, 18802 and 19543 instances respectively were used for the purpose. Validation has been done using measures like ROC curve and accuracy. The experimental results showed that all the classifiers showing excellent results with random forest outperforming the other two. The effectiveness of random forest in classifying strokes which are overlapping in nature is done by comparing the known results of that with multi-layer perceptron.

show abstract

Data mining techniques on astronomical spectra data – II. Classification analysis

Yang

Zhou

Cai

et al. 2022

Monthly Notices of the Royal Astronomical Society

View full text Add to dashboard Cite

Classification is valuable and necessary in spectral analysis, especially for data-driven mining. Along with the rapid development of spectral surveys, a variety of classification techniques have been successfully applied to astronomical data processing. However, it is difficult to select an appropriate classification method in practical scenarios due to the different algorithmic ideas and data characteristics. Here, we present the second work in the data mining series - a review of spectral classification techniques. This work also consists of three parts: a systematic overview of current literature, experimental analyses of commonly used classification algorithms and source codes used in this paper. Firstly, we carefully investigate the current classification methods in astronomical literature and organize these methods into ten types based on their algorithmic ideas. For each type of algorithm, the analysis is organized from the following three perspectives. 1) their current applications and usage frequencies in spectral classification are summarized; 2) their basic ideas are introduced and preliminarily analysed; 3) the advantages and caveats of each type of algorithm are discussed. Secondly, the classification performance of different algorithms on the unified datasets is analysed. Experimental data are selected from the LAMOST survey and SDSS survey. Six groups of spectral datasets are designed from data characteristics, data qualities and data volumes to examine the performance of these algorithms. Then the scores of nine basic algorithms are shown and discussed in the experimental analysis. Finally, nine basic algorithms source codes written in python and manuals for usage and improvement are provided.

show abstract

Application of Decision Trees for Classifying Astronomical Objects

Cited by 4 publications

References 10 publications

Cervical Cancer Prediction through Different Screening Methods using Data Mining

Cervical Cancer Prediction through Different Screening Methods using Data Mining

Tree-Based Classification of Tabla Strokes

Data mining techniques on astronomical spectra data – II. Classification analysis

Contact Info

Product

Resources

About