Optimization of C4.5 algorithm-based particle swarm optimization for breast cancer diagnosis

Muslim, Much Aziz; Rukmana, Siti Hardiyanti; Sugiharti, Endang; Prasetiyo, Budi; Alimah, Siti

doi:10.1088/1742-6596/983/1/012063

Cited by 25 publications

(32 citation statements)

References 5 publications

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“…The above improvement of information gain is the processing of the influence relationship between attributes and targets. Although C4.5 adopts split information to solve the problem of selection attributes with more values in the modeling process, but it does not take into account the influence of redundancy between attributes [21][22][23]. And division of information as a partition impurity measure, which build the gain rate tend to high purity of samples and contains most of the subset of the total sample into two side partition, this will lead to the problem, the problem that will result is that it is possible to mistakenly focus on some samples that are farther from the center [22,23].…”

Section: B C45 Improved Processing Of Split Informationmentioning

confidence: 99%

An Improved C4.5 Algorthm in Bagging Integration Model

Song

Liu

et al. 2020

IEEE Access

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The C4.5 algorithm has three shortcomings: the wide range of candidate segmentation threshold sequences for continuous attributes, the comprehensive influence of different attributes and local subsets under the same attribute, and the inter-attribute redundancy. When dealing with continuous attributes, sampling and threshold supplement processing near the transition boundary of the attribute interval corresponding to the adjacent different categories are performed for narrowing the range of candate segmentation threshold sequences. By adding standardizing Euclidean distance of the attribute global and local factors to represent attribute weight, the calculation of C4.5 information gain is otpimized. And averaging Gini index of other attributes and adding correction factor, the influence of redundancy between attributes is greatly decreased. The overall average improvement range of the base classifier and the bagging integration classifier is 0.6% ~ 2.1% and 0.7% ~ 2.7%, respectively, which shows that this integration model can improve the classification accuracy and also validate its feasibility and reliability.

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Section: B C45 Improved Processing Of Split Informationmentioning

confidence: 99%

An Improved C4.5 Algorthm in Bagging Integration Model

Song

Liu

et al. 2020

IEEE Access

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“…In the classification phase of sentiment analysis using SVM based on 10-fold cross validation training data and test data divided on 10-fold cross validation iteration [23], so that the learning and testing stage is done in 10-fold cross validation iteration as follows. 6) The final result will be obtained an output that is the level of accuracy.…”

Section: Mining Processmentioning

confidence: 99%

Improve the Accuracy of Support Vector Machine Using Chi Square Statistic and Term Frequency Inverse Document Frequency on Movie Review Sentiment Analysis

Larasati

Muslim

Arifudin

et al. 2019

SJI

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Data processing can be done with text mining techniques. To process large text data is required a machine to explore opinions, including positive or negative opinions. Sentiment analysis is a process that applies text mining methods. Sentiment analysis is a process that aims to determine the content of the dataset in the form of text is positive or negative. Support vector machine is one of the classification algorithms that can be used for sentiment analysis. However, support vector machine works less well on the large-sized data. In addition, in the text mining process there are constraints one is number of attributes used. With many attributes it will reduce the performance of the classifier so as to provide a low level of accuracy. The purpose of this research is to increase the support vector machine accuracy with implementation of feature selection and feature weighting. Feature selection will reduce a large number of irrelevant attributes. In this study the feature is selected based on the top value of K = 500. Once selected the relevant attributes are then performed feature weighting to calculate the weight of each attribute selected. The feature selection method used is chi square statistic and feature weighting using Term Frequency Inverse Document Frequency (TFIDF). Result of experiment using Matlab R2017b is integration of support vector machine with chi square statistic and TFIDF that uses 10 fold cross validation gives an increase of accuracy of 11.5% with the following explanation, the accuracy of the support vector machine without applying chi square statistic and TFIDF resulted in an accuracy of 68.7% and the accuracy of the support vector machine by applying chi square statistic and TFIDF resulted in an accuracy of 80.2%.

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“…Two studies applied Particle Swarm Optimization for improving the feature selection and modeling using Decision Tree (C4.5) to improve the accuracy of early detection and Naï ve Bayes for early recurrence prediction [13], [14]. Another study have shown improvement in the SVM has highest specificity, accuracy (97%) and precision (97%) but RF has greater probability of discriminating between benign and malignant tumors with ROC of 99.9% [15].…”

Section: A Wbc Datasetsmentioning

confidence: 99%

Feasibility Study on Data Mining Techniques in Diagnosis of Breast Cancer

Rajendran¹,

Jayabalan²,

Thiruchelvam³

et al. 2019

IJMLC

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Survivability of patients suffering from breast cancer varies according to the stages. The early detection of breast cancer increase the longevity of patients. However, the number of risk factors involved in the detection exponentially increases with the medical examinations. The need for automated data mining techniques to enable cost-effective and early prediction of cancer is rapidly becoming a trend in healthcare industry. The optimal techniques for prediction and diagnosis differs significantly due to the risk factors. This study reviews article provides a holistic view of the types of data mining techniques used in prediction of breast cancer. On a whole, the computer-aided automatic data mining techniques that are commonly employed in diagnosis and prognosis of chronic diseases include Decision Tree, Naï ve Bayes, Association rule, Multilayer Perceptron (MLP), Random Forest, and Support Vector Machines (SVM), among others. The accuracy and overall performance of the classifiers differ for every dataset and thereby this article attempts to provide a mean to understand the approaches involved in the early prediction.

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Optimization of C4.5 algorithm-based particle swarm optimization for breast cancer diagnosis

Cited by 25 publications

References 5 publications

An Improved C4.5 Algorthm in Bagging Integration Model

An Improved C4.5 Algorthm in Bagging Integration Model

Improve the Accuracy of Support Vector Machine Using Chi Square Statistic and Term Frequency Inverse Document Frequency on Movie Review Sentiment Analysis

Feasibility Study on Data Mining Techniques in Diagnosis of Breast Cancer

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