Comparative Analysis of Discretization Algorithms on Decision Tree

Thaiphan, Rattayagon; Phetkaew, Thimaporn

doi:10.1109/icis.2018.8466449

Cited by 9 publications

(7 citation statements)

References 14 publications

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“…The usual technique is used for the same width and the frequency is the same, which contain and generate the sum of intervals. This technique is stated with the similar sum of transaction correspondingly and transforming numerical input or output variables to have discrete ordinal labels [ 15 ]. On contrary, there are two types of discretization including univariate and multivariate.…”

Section: Background and Related Workmentioning

confidence: 99%

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An Approach to Acquire the Constraints Using Panel Big Data Hybrid Association Rule and Discretization Process for Breast Cancer Prediction

Althunibat

Alzyadat

Muhairat

et al. 2021

Journal of Healthcare Engineering

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In recent years, big data has become an important branch of computer science. However, without AI, it is difficult to dive into the context of data as a prediction term, relying on a large feature of improving the process of prediction is connected with big data modelling, which appears to be a significant aspect of improving the process of prediction. Accordingly, one of the basic constructions of the big data model is the rule-based method. Rule-based method is used to discover and utilize a set of association rules that collectively represent the relationships identified by the system. This work focused on the use of the Apriori algorithm for the investigations of constraints from panel data using the discretization preprocess technique. The statistical outcomes are associated with the improved preprocess that can be applied over the transaction and it can illustrate interesting rules with confidence approximately equal to one. The minimum support provided to the present rule considers constraint as a milestone for the prediction model. The model makes an effective and accurate decision. In nowadays business, several guidelines have been produced. Moreover, the generation method was upgraded because of an association data algorithm that works for dissimilar principles of the structures compared with fewer breaks that are delivered by the discretization technique.

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Section: Background and Related Workmentioning

confidence: 99%

“…The intersection between X and Y processes the unfilled set. It consists of two significant methods, controls a link among the transaction of items, and supports every degree in the dissimilar self-assurance [ 15 ]. The sustenance for regulation X !…”

Section: Background and Related Workmentioning

confidence: 99%

An Approach to Acquire the Constraints Using Panel Big Data Hybrid Association Rule and Discretization Process for Breast Cancer Prediction

Althunibat

Alzyadat

Muhairat

et al. 2021

Journal of Healthcare Engineering

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show abstract

“…The aim of attribute discretization is to search out concise data representations as categories that are sufficient for the training task to retain the maximum amount information as possible within the original continuous attributes. The foremost common method is to partition each feature into two or more intervals using all values of the attribute through multiple cutting points [16]. There will be a DA discretization scheme in the continuous attribute A, which divides this attribute into k discrete and disjoint intervals…”

Section: The Comprehensive the Oretical Basismentioning

confidence: 99%

“…The most common method is to partition each feature into two or more intervals using all values of the attribute through multiple cutting points. Therefore, the numerical features are divided into intervals and the discrete intervals behave like categorical values [16]. The discretization results are the generation of crisp intervals so that a feature value either belongs to an interval or not.…”

Section: Introductionmentioning

confidence: 99%

Scalable decision tree based on fuzzy partitioning and an incremental approach

Lotfi

Ghasemzadeh

Mohsenzadeh

et al. 2022

IJECE

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<span>Classification as a data mining materiel is the process of assigning entities to an already defined class by examining the features. The most significant feature of a decision tree as a classification method is its ability to data recursive partitioning. To choose the best attributes for partition, the value range of each continuous attribute should be divided into two or more intervals. Fuzzy partitioning can be used to reduce noise sensitivity and increase the stability of trees. Also, decision trees constructed with existing approaches, tend to be complex, and consequently are difficult to use in practical applications. In this article, a fuzzy decision tree has been introduced that tackles the problem of tree complexity and memory limitation by incrementally inserting data sets into the tree. Membership functions are generated automatically. Then Fuzzy Information Gain is used as a fast-splitting attribute selection criterion and the expansion of a leaf is done attending only with the instances stored in it. The efficiency of this algorithm is examined in terms of accuracy and tree complexity. The results show that the proposed algorithm by reducing the complexity of the tree can overcome the memory limitation and make a balance between accuracy and complexity.</span>

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“…Depending on the nature of data, there are many methods of data pre-processing that can be applied and data discretization is one of those methods [2]. Data discretization is applied when certain machine learning algorithm can produce better result like classification accuracy, with discrete data [3]. Thus, when using these types of classification algorithms, it is necessary to discretize continuous data in the dataset used.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Fuzzy Discretization’s Output on Classification Accuracy of Random Forest Classifier

Fikri¹

2020

IJATCSE

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Random Forest is known as among the widely used classification algorithms by researchers and machine learning enthusiast in solving classification problems. Recently, fuzzy discretization has been paired with Random Forest (RF) classifier to enhance the classification accuracy of Random Forest classifier when dealing with continuous variables. However, there are many different opinions on whether there is a need to perform discretization in data pre-processing for tree-based classifiers such as J48, Decision Tree and Random Forest. On top of that, it is known that different classification algorithms produce different classification accuracies depending on the type of data used. In other words, the output of data discretization process. Thus, to unravel this mentioned hypothesis, this study intends to shed some lights on the impact of different fuzzy discretization's output on the classification accuracy of Random Forest classifier. In this study, three version of simulations were done with different fuzzy discretization output. Those fuzzy discretization's outputs are 1) without fuzzy discretization 2) with fully fuzzy discretization and 3) with partial fuzzy discretization. Then, classification phase is done through Random Forest classifier and the classification accuracy for all the simulation versions were observed, recorded, and analyzed.

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Comparative Analysis of Discretization Algorithms on Decision Tree

Cited by 9 publications

References 14 publications

An Approach to Acquire the Constraints Using Panel Big Data Hybrid Association Rule and Discretization Process for Breast Cancer Prediction

An Approach to Acquire the Constraints Using Panel Big Data Hybrid Association Rule and Discretization Process for Breast Cancer Prediction

Scalable decision tree based on fuzzy partitioning and an incremental approach

The Impact of Fuzzy Discretization’s Output on Classification Accuracy of Random Forest Classifier

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