scholarly journals A Binary Decision Tree Abstract Domain Functor

2015 ◽  
pp. 36-53 ◽  
Author(s):  
Junjie Chen ◽  
Patrick Cousot
Keyword(s):  
Decision Tree ◽  
Binary Decision ◽  
Binary Decision Tree

scholarly journals An Algorithm for Notifiable Disease Modeling and Prediction Using Artificial Intelligence Techniques: A Case of Kenya.

2021 ◽  
Author(s):  
Nicodemus Nzoka Maingi ◽  
Ismail Ateya Lukandu ◽  
Matilu MWAU
Keyword(s):  
Artificial Intelligence ◽  
Decision Tree ◽  
Disease Modeling ◽  
Information Gain ◽  
Disease Outbreak ◽  
Notifiable Disease ◽  
Data Set ◽  
Great Opportunity ◽  
Binary Decision ◽  
Binary Decision Tree

Abstract BackgroundThe disease outbreak management operations of most countries (notably Kenya) present numerous novel ideas of how to best make use of notifiable disease data to effect proactive interventions. Notifiable disease data is reported, aggregated and variously consumed. Over the years, there has been a deluge of notifiable disease data and the challenge for notifiable disease data management entities has been how to objectively and dynamically aggregate such data in a manner such as to enable the efficient consumption to inform relevant mitigation measures. Various models have been explored, tried and tested with varying results; some purely mathematical and statistical, others quasi-mathematical cum software model-driven.MethodsOne of the tools that has been explored is Artificial Intelligence (AI). AI is a technique that enables computers to intelligently perform and mimic actions and tasks usually reserved for human experts. AI presents a great opportunity for redefining how the data is more meaningfully processed and packaged. This research explores AI’s Machine Learning (ML) theory as a differentiator in the crunching of notifiable disease data and adding perspective. An algorithm has been designed to test different notifiable disease outbreak data cases, a shift to managing disease outbreaks via the symptoms they generally manifest. Each notifiable disease is broken down into a set of symptoms, dubbed symptom burden variables, and consequently categorized into eight clusters: Bodily, Gastro-Intestinal, Muscular, Nasal, Pain, Respiratory, Skin, and finally, Other Symptom Clusters. ML’s decision tree theory has been utilized in the determination of the entropies and information gains of each symptom cluster based on select test data sets.ResultsOnce the entropies and information gains have been determined, the information gain variables are then ranked in descending order; from the variables with the highest information gains to those with the lowest, thereby giving a clear-cut criteria of how the variables are ordered. The ranked variables are then utilized in the construction of a binary decision tree, which graphically and structurally represents the variables. Should any variables have a tie in the information gain rankings, such are given equal importance in the construction of the binary decision-tree. From the presented data, the computed information gains are ordered as; Gastro-Intestinal, Bodily, Pain, Skin, Respiratory, Others. Muscular, and finally Nasal Symptoms respectively. The corresponding binary decision tree is then constructed.ConclusionsThe algorithm successfully singles out the disease burden variable(s) that are most critical as the point of diagnostic focus to enable the relevant authorities take the necessary, informed interventions. This algorithm provides a good basis for a country’s localized diagnostic activities driven by data from the reported notifiable disease cases. The algorithm presents a dynamic mechanism that can be used to analyze and aggregate any notifiable disease data set, meaning that the algorithm is not fixated or locked on any particular data set.

scholarly journals Using Minimum Local Distortion to Hide Decision Tree Rules

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 334 ◽  
Author(s):  
Georgios Feretzakis ◽  
Dimitris Kalles ◽  
Vassilios Verykios
Keyword(s):  
Decision Tree ◽  
Electronic Commerce ◽  
Decision Trees ◽  
Heuristic Approach ◽  
Common Procedure ◽  
Raw Data ◽  
Binary Decision ◽  
Local Distortion ◽  
Binary Decision Tree ◽  
Cryptographic Techniques

The sharing of data among organizations has become an increasingly common procedure in several areas like banking, electronic commerce, advertising, marketing, health, and insurance sectors. However, any organization will most likely try to keep some patterns hidden once it shares its datasets with others. This article focuses on preserving the privacy of sensitive patterns when inducing decision trees. We propose a heuristic approach that can be used to hide a certain rule which can be inferred from the derivation of a binary decision tree. This hiding method is preferred over other heuristic solutions like output perturbation or cryptographic techniques—which limit the usability of the data—since the raw data itself is readily available for public use. This method can be used to hide decision tree rules with a minimum impact on all other rules derived.

Fuzzy binary decision tree for biometric based personal authentication

2013 ◽  
Vol 99 ◽  
pp. 87-97 ◽  
Author(s):  
Amioy Kumar ◽  
M. Hanmandlu ◽  
H.M. Gupta
Keyword(s):  
Decision Tree ◽  
Binary Decision ◽  
Binary Decision Tree ◽  
Personal Authentication

Freshness Classification of Horse Mackerels with E-Nose System Using Hybrid Binary Decision Tree Structure

2019 ◽  
Vol 34 (03) ◽  
pp. 2050003
Author(s):  
Selda Güney ◽  
Ayten Atasoy
Keyword(s):  
Decision Tree ◽  
Electronic Nose ◽  
Low Cost ◽  
Storage Conditions ◽  
Tree Structure ◽  
Training Dataset ◽  
Support Vector ◽  
Binary Decision ◽  
Linear Discriminant ◽  
Binary Decision Tree

The aim of this study is to test the freshness of horse mackerels by using a low cost electronic nose system composed of eight different metal oxide sensors. The process of freshness evaluation covers a scala of seven different classes corresponding to 1, 3, 5, 7, 9, 11, and 13 storage days. These seven classes are categorized according to six different classifiers in the proposed binary decision tree structure. Classifiers at each particular node of the tree are individually trained with the training dataset. To increase success in determining the level of fish freshness, one of the k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA) and Bayes methods is selected for every classifier and the feature spaces change in every node. The significance of this study among the others in the literature is that this proposed decision tree structure has never been applied to determine fish freshness before. Because the freshness of fish is observed under actual market storage conditions, the classification is more difficult. The results show that the electronic nose designed with the proposed decision tree structure is able to determine the freshness of horse mackerels with 85.71% accuracy for the test data obtained one year after the training process. Also, the performances of the proposed methods are compared against conventional methods such as Bayes, k-NN, and LDA.

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