The Analytical Comparison of ID3 and C4.5 using WEKA

Nijhawan, Vani Kapoor; Madan, Mamta; Dave, Meenu

doi:10.5120/ijca2017914286

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…The decision tree learning algorithm that would be studied in this paper is ID3 algorithm as it is the most commonly implement learning algorithm at the moment (Chen, Luo & Mu, 2009;Liu & Wang, 2010;Luo, Chen & Zhang, 2010). Quinlan invented the ID3 algorithm that also known as Iterative Dichotomiser 3 in 1986 (Liu & Xie, 2010;Nijhawan, Madan & Dave, 2017). Theoretically, ID3 algorithm function based on recursive partitioning which the training data would undergo splitting to become subsets and the particular subsets become the partitions that depict the decision tree (Begenova & Avdeenko, 2018a;Wu et al, 2006).…”

Section: Decision Tree: Id3 Algorithmmentioning

confidence: 99%

Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition

Idris¹,

Ismail²

2021

PeerJ Computer Science

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Breast cancer becomes the second major cause of death among women cancer patients worldwide. Based on research conducted in 2019, there are approximately 250,000 women across the United States diagnosed with invasive breast cancer each year. The prevention of breast cancer remains a challenge in the current world as the growth of breast cancer cells is a multistep process that involves multiple cell types. Early diagnosis and detection of breast cancer are among the greatest approaches to preventing cancer from spreading and increasing the survival rate. For more accurate and fast detection of breast cancer disease, automatic diagnostic methods are applied to conduct the breast cancer diagnosis. This paper proposed the fuzzy-ID3 (FID3) algorithm, a fuzzy decision tree as the classification method in breast cancer detection. This study aims to resolve the limitation of an existing method, ID3 algorithm that unable to classify the continuous-valued data and increase the classification accuracy of the decision tree. FID3 algorithm combined the fuzzy system and decision tree techniques with ID3 algorithm as the decision tree learning. FUZZYDBD method, an automatic fuzzy database definition method, would be used to design the fuzzy database for fuzzification of data in the FID3 algorithm. It was used to generate a predefined fuzzy database before the generation of the fuzzy rule base. The fuzzified dataset was applied in FID3 algorithm, which is the fuzzy version of the ID3 algorithm. The inference system of FID3 algorithm is simple with direct extraction of rules from generated tree to determine the classes for the new input instances. This study also analysed the results using three breast cancer datasets: WBCD (Original), WDBC (Diagnostic) and Coimbra. Furthermore, the comparison of FID3 algorithm with the existing methods is conducted to verify the proposed method’s capability and performance. This study identified that the combination of FID3 algorithm with FUZZYDBD method is reliable, robust and managed to perform well in breast cancer classification.

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Section: Decision Tree: Id3 Algorithmmentioning

confidence: 99%

Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition

Idris¹,

Ismail²

2021

PeerJ Computer Science

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“…This algorithm is only able to classify data with a range of discrete and limited features and is not efficient for noisy and distorted data. Completed C4.5 algorithm is ID3 algorithm [2][3][4]. This algorithm is also able to classify continuous and noisy data.…”

Section: C45 Algorithmmentioning

confidence: 99%

Improving the Performance of Distance Relay Using Wavelet Transform

Mohabati

Khoddam

2021

Teh. glas. (Online)

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With proliferation of power grids, different types of faults are more likely to occur. The purpose of system protection relays is to detect abnormal signals that indicate faults in the transmission system and to separate the fault section from the rest of the system to prevent the fault from propagating to other parts of the system. Proliferation of electronic devices led to creation of digital relays made of microprocessors. Hence, analog measurements are converted into digital signals for processing by microprocessors. Air grids are more likely to cause faults than other components of the power system; thus, disturbances affecting the system must be detected quickly and accurately. Therefore, the problem of fault detection and classification is an important factor for economic exploitation of the power grid. Accurate fault handling results in faster repair operations, better system availability, lower operating costs and timesavings. The proposed design in this study was based on detecting the type of fault caused in transmission lines. In order to improve the condition of the relays in the transmission grid, analysis of the signals reached to the relay on both sides of the line was used to detect the fault and its type. The main purpose was to quickly detect the type of fault using wavelet transform. For this purpose, the signal was sampled after the fault occurs and the feature signal was extracted after analysis by wavelet transform. These features were included in the decision tree classifier and the type of the fault was decided.

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“…Many other researchers have researched on decision trees. Among them is a study comparing several methods about decision trees [6] [7]. Research using this decision tree method can be applied in many fields.…”

Section: B Related Workmentioning

confidence: 99%

Covid Symptom Severity Using Decision Tree

Rochmawati

Hidayati

Yamasari

et al. 2020

2020 Third International Conference on Vocational Education and Electrical Engineering (ICVEE)

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Corona is a very contagious virus. In a pandemic like this, people often worry whether they are infected or not. When they cough, they often worry whether it is a sign of covid-19 or an ordinary cough. From the clinical symptoms can actually be known whether someone has Covid or not. In this study, a clinical symptom dataset will be used to classify the symptoms using a Decision Tree algorithm. The decision trees used in this research are J48 and Hoeffding Tree. Decision Tree is one of the most popular classification methods because it is easy to interpret by Humans. the prediction model uses a hierarchical structure. The concept is to convert data into decision trees or decision rules. the result of J48 were slightly better than the Hoeffding tree in terms of accuracy, precision, and recall. Meanwhile, from the tree view results, the Hoeffding Tree is simpler and the number of nodes is less than J48.

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The Analytical Comparison of ID3 and C4.5 using WEKA

Cited by 6 publications

References 3 publications

Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition

Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition

Improving the Performance of Distance Relay Using Wavelet Transform

Covid Symptom Severity Using Decision Tree

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