Fuzzy Heuristics and Decision Tree for Classification of Statistical Feature-Based Control Chart Patterns

Zaman, Munawar; Hassan, Adnan

doi:10.3390/sym13010110

Cited by 17 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Extracting statistical features, the work of Zaman et al [112] builds a decision tree for the classification of control chart patterns (univariate time series). The decision tree is assumed to be interpretable and is shown as an example.…”

Section: Instance-based Explanationsmentioning

confidence: 99%

Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions

et al. 2022

View full text Add to dashboard Cite

Time series data is increasingly used in a wide range of fields, and it is often relied on in crucial applications and high-stakes decision-making. For instance, sensors generate time series data to recognize different types of anomalies through automatic decision-making systems. Typically, these systems are realized with machine learning models that achieve top-tier performance on time series classification tasks. Unfortunately, the logic behind their prediction is opaque and hard to understand from a human standpoint. Recently, we observed a consistent increase in the development of explanation methods for time series classification justifying the need to structure and review the field. In this work, we (a) present the first extensive literature review on Explainable AI (XAI) for time series classification, (b) categorize the research field through a taxonomy subdividing the methods into time points-based, subsequences-based and instance-based, and (c) identify open research directions research directions regarding the type of explanations and the evaluation of explanations and interpretability.

show abstract

Section: Instance-based Explanationsmentioning

confidence: 99%

Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Therefore, the structure function building can be interpreted as a classification task for incomplete or uncertain data. It is a typical task of Data Mining [37,44,45]. One of the approaches for solving this task is the application of a Fuzzy Decision Tree [14,44].…”

Section: Structure Functionmentioning

confidence: 99%

“…In this paper, a new method for the structure function construction based on uncertain and incomplete data is considered. This method was developed based on knowledge of reliability engineering [26,27,42] and Data Mining [37,44,45]. The proposed method, from a point of view of reliability analysis, was elaborated taking into account the problem of the system mathematical model representation based on uncertain data.…”

Section: Related Studiesmentioning

confidence: 99%

Reliability Evaluation of the Factors That Influenced COVID-19 Patients’ Condition

2021

View full text Add to dashboard Cite

Health and safety is a problem that is intensively discussed nowadays. The failures in healthcare are called medical errors: if the patient’s condition worsens or he/she contracts an illness, then the actions that led to this are interpreted as medical errors. Medical errors can be the result of new procedures, extremes of age, complex or urgent care, improper documentation, illegible hand-writing, or patient actions. One of the ways to reduce medical error is an evaluation of its possibility, and then using the result of this evaluation to improve the medical organization units and processes in patient diagnosis, treatment, and care. This evaluation is possible based on methods of reliability engineering. The reliability engineering methods allow evaluating of different systems’ reliability and the influence of external and internal factors on system reliability. These methods’ application needs the system to be investigated or objective interpretation in terms of reliability engineering. Therefore, such a system in healthcare, for the diagnosis of disease, a patient’s treatment, the influence of different factors on a patient’s condition, and others, should be presented according to the rules and demands of reliability engineering. The first step is development of the mathematical representation of the investigated system or object according to the demands of the reliability analysis. One of the often-used mathematical representations in the reliability analysis of a system is the structure function. However, this mathematical representation needs completely specified initial data. The initial data from the healthcare domain for medical error analysis is uncertain and incompletely specified. Therefore, the development of this mathematical representation needs special methods. In this paper, a new method for the mathematical representation of system development based on uncertain and incompletely specified data is proposed. The system evaluation based on the structure function allows computing of many reliability indices and measures used in reliability engineering. The approbation of this method is considered based on an example of COVID-19 patients.

show abstract

“…Each branch signifies a test effect, and every leaf node controls a class of labels. The DT learns by separating a basis set into subsections, depending on the value of a test element [28].…”

Section: A Decision Tree (Dt)mentioning

confidence: 99%

An Intelligent Detection System for Covid-19 Diagnosis Using Ct-Images

Hasan

Kader

Hossam

2021

JES. Journal of Engineering Sciences

View full text Add to dashboard Cite

Early classification of the Coronavirus disease (COVID-19) is necessary to control its rapid spread and save patients' lives. The fast spread of COVID-19 has increased the diagnostic encumbrance of radiologists. Therefore, clinicians need to quickly assess if a patient has COVID-19 or not. Artificial Intelligence (AI) has shown promising results in healthcare. So, this paper proposed a computer-aided intelligence model that can identify positive COVID-19 cases. It presented the pipeline of medicinal imaging and examination methods involved in COVID-19 image acquirement, segmentation, and diagnosis, using Computed Tomography (CT) images. This paper introduced two effective models for single machine learning (SML) and ensemble machine learning (EML) with 10-fold cross validation, to detect cases of COVID-19. The first Amira M. Hasan et al., An Intelligent Detection System for Covid-19 Diagnosis….classification model (SML) was applied with different algorithms, such as Decision Tree (DT), Artificial Neural Networks (ANN), and Support Vector Machines (SVM). Results showed that the performance of the SVM surpassed other classifiers with a 98.85 % accuracy. The second classification model (EML) was applied with several algorithms, such as Random Forest (RF), Voting, and Bagging, to increase its accuracy up to 99.60%, especially using the Bagging classifier. Finally, the results of the two proposed models showed better performance compared with other recent studies. However, the EML showed an even better performance than SML and is recommended for use in real-time.

show abstract

Fuzzy Heuristics and Decision Tree for Classification of Statistical Feature-Based Control Chart Patterns

Cited by 17 publications

References 22 publications

Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions

Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions

Reliability Evaluation of the Factors That Influenced COVID-19 Patients’ Condition

An Intelligent Detection System for Covid-19 Diagnosis Using Ct-Images

Contact Info

Product

Resources

About