Decision tree classification with bounded number of errors

Saettler, Aline Medeiros; Laber, Eduardo Sany; Pereira, Felipe de A. Mello

doi:10.1016/j.ipl.2017.06.011

Cited by 20 publications

(4 citation statements)

References 2 publications

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“…The decision tree has several terms. Those terms are a root as the initial node, a leaf node as the child of a node, and the depth of a node as the length of the path between the nodes to the leaf node [27,28]. The first step is to discover graph process model of the event log based on the graph database.…”

Section: Methodsmentioning

confidence: 99%

Graph-based algorithm for checking wrong indirect relationships in non-free choice

2020

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In this context, this paper proposes a combination of parameterised decision mining and relation sequences to detect wrong indirect relationship in the non-free choice. The existing decision mining without parameter can only detect the direction, but not the correctness. This paper aims to identify the direction and correctness with decision mining with parameter. This paper discovers a graph process model based on the event log. Then, it analyses the graph process model for obtaining decision points. Each decision point is processed by using parameterised decision mining, so that decision rules are formed. The derived decision rules are used as parameters of checking wrong indirect relationship in the non-free choice. The evaluation shows that the checking wrong indirect relationships in non-free choice with parameterised decision mining have 100% accuracy, whereas the existing decision mining has 90.7% accuracy.

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Section: Methodsmentioning

confidence: 99%

Graph-based algorithm for checking wrong indirect relationships in non-free choice

2020

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“…Image classifiers of various sorts have been developed over the years using a variety of different methods such as decision trees [ 5 , 6 ], neural networks [ 7 , 8 ], or support vector machines [ 9 ]. Evolutionary algorithms have gained increased popularity for classification tasks [ 10 ] due to the simplicity with which fitness functions can be expressed, and also the variety of the expressions (linear, non-linear, tree-based, etc.).…”

Section: Introductionmentioning

confidence: 99%

On the use of multi–objective evolutionary classifiers for breast cancer detection

Dioşan

Andreica²,

Voiculescu³

2022

PLoS ONE

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Purpose Breast cancer is one of the most common tumours in women, nevertheless, it is also one of the cancers that is most usually treated. As a result, early detection is critical, which can be accomplished by routine mammograms. This paper aims to describe, analyze, compare and evaluate three image descriptors involved in classifying breast cancer images from four databases. Approach Multi–Objective Evolutionary Algorithms (MOEAs) prove themselves as being efficient methods for selection and classification problems. This paper aims to study combinations of well–known classification objectives in order to compare the results of their application in solving very specific learning problems. The experimental results undergo empirical analysis which is supported by a statistical approach. The results are illustrated on a collection of medical image databases, but with a focus on the MOEAs’ performance in terms of several well–known measures. The databases were chosen specifically to feature reliable human annotations, so as to measure the correlation between the gold standard classifications and the various MOEA classifications. Results We have seen how different statistical tests rank one algorithm over the others in our set as being better. These findings are unsurprising, revealing that there is no single gold standard for comparing diverse techniques or evolutionary algorithms. Furthermore, building meta-classifiers and evaluating them using a single, favorable metric is both extremely unwise and unsatisfactory, as the impact is to skew the results. Conclusions The best method to address these flaws is to select the right set of objectives and criteria. Using accuracy-related objectives, for example, is directly linked to maximizing the number of true positives. If, on the other hand, accuracy is chosen as the generic metric, the primary classification goal is shifted to increasing the positively categorized data points.

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“…Algorithms like chi‐squared automatic interaction detector and conditional inference trees() make DT one of most powerful and useful algorithm for data mining. The benefit of DT is that it can handle different types of input data such as numerical, alphabetical, and nominal …”

Section: Introductionmentioning

confidence: 99%

“…The algorithm for generating a DT for a given data set is fully described in the works of Saettler et al and California State University, Northridge…”

Section: Introductionmentioning

confidence: 99%

Hybrid machine learning algorithms for fault detection in android smartphones

Jogarah

Soopaul

Beeharry

et al. 2018

Trans Emerging Tel Tech

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The newest trends with smart devices and the concept of Internet of Things (IoT) is that of big data and analytics. More recently, the concept of mobile cloud computing has emerged with advances in terms of hardware and communication speeds. However, there are serious implications on the normal lifetime of the battery‐powered smart devices being used for these different applications. Most sensors deployed for different smart IoT applications are already available in smart phones and used by the majority of individuals on a daily basis. With the recent explosions of the Samsung Galaxy Note 7 smartphones, safety of individuals has climbed the priority list in the IoT field. Along this line, it is important to address fault detection and prediction of smartphones, which are being used in fields such as mobile health. In this paper, a cloud‐based open‐source framework for capturing and processing real‐time streaming information (eg, screen brightness, CPU usage, battery level, voltage, device temperature, and Wi‐Fi signal strength) about different android‐based smartphones has been set up. A NoSQL Cassandra database is used and a Spark distributed computing Scala‐based framework accesses the data for further processing. Classification machine learning algorithms (Naïve‐Bayes (NB), decision tree (DT), and random forest) are used to obtain trained models for predicting symptoms of faulty behaviors in smartphones. The performance of a hybrid 2‐staged machine learning mechanism is proposed whereby cascaded classification algorithms are used. Results show that when using DT algorithm (Level 1) and NB algorithm (Level 2), a slight increase in percentage accuracy is observed. This demonstrates that there is the possibility for further improving the NB model for classification and fault detection when using a combined training model with DT algorithm (Level 1) and NB algorithm (Level 2). The results for the cascaded model show that the percentage accuracy of most hybrid models in this case are slightly inferior to the standalone models themselves. The hybrid models using the DT algorithm at both levels and DT algorithm followed by RF algorithm do not improve the percentage accuracy. However, this demonstrates that the robustness of the classification algorithm can be maintained with this type of hybrid classification algorithm and leaves room for further research of possible techniques and ways to improve the accuracy.

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Decision tree classification with bounded number of errors

Cited by 20 publications

References 2 publications

Graph-based algorithm for checking wrong indirect relationships in non-free choice

Graph-based algorithm for checking wrong indirect relationships in non-free choice

On the use of multi–objective evolutionary classifiers for breast cancer detection

Hybrid machine learning algorithms for fault detection in android smartphones

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