An online software for decision tree classification and visualization using c4.5 algorithm (ODTC)

Das, Suvajit; Dahiya, Shashi; Bharadwaj, Anshu

doi:10.1109/indiacom.2014.6828107

Cited by 8 publications

(7 citation statements)

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“…When analyzing Table III, it possible to conclude that the best classification algorithms, at the performance level, are as follows: trees.RandomForest [16] [14], lazy.IBk [15] [13], trees.J48 [9] [12] and rules.PART [11] [18]. We have obtained figures, like the one shown in Figure 5, to the analyse the best nights resulting from the study for the selected four algorithms/models.…”

Section: Tests and Resultsmentioning

confidence: 99%

Non-invasive monitoring with Ballistocardiographic sensors for sleep management

Silva

Marinheiro

2021

2021 Telecoms Conference (ConfTELE)

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Sleep has an important impact on people's daily lives. A successful methodology for monitoring sleep is Polysomnography (PSG). This is an accurate and reliable approach but, unfortunately, very invasive. PSG uses expensive sensors that must be positioned by experts, what, in practice, makes its adoption only viable in hospital setups. Therefore, there is a demand for better non-invasive alternatives, such as Ballistocardiography (BCG). BCG uses cheaper sensors, easy to install and ideal for domestic use. This allows its integration in solutions that manage sleep, using mobile apps not only for presenting valuable information to users but may also for acting on the environment, through actuators, such as sound.This work uses this principle to help users to wake up smoothly. Sleep monitoring is performed with Murata SCA11H BCG external sensors. Low-pass filters have been implemented, using a sliding exponential average, for all metrics. The Random Forest algorithm was then selected for sleep phase classification, that presented the best performance when using the Weka exploration tool for learning methods. With the implemented model, it has been proved that four sleep phases are predicted. It was then possible to define a strategy for avoiding waking up alarms to be fired during deep sleep. It consists on the analysis 15 minutes prior to the alarm and, when deep sleep is detected, a relaxing sound is played.This work demonstrated that non-invasive sleep monitoring can be used to actuate on, and improve, the user environment, in a home setup with cheap sensors.

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Section: Tests and Resultsmentioning

confidence: 99%

Non-invasive monitoring with Ballistocardiographic sensors for sleep management

Silva

Marinheiro

2021

2021 Telecoms Conference (ConfTELE)

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“…A tree model is a decision support tool that uses a tree-like diagram or model to represent a decision and its possible consequences, including chance event outcomes, costs of resources, and utility. [ 33 ]…”

Section: Discussionmentioning

confidence: 99%

Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree

Shi

Liu

et al. 2021

Medicine

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Sepsis is a leading cause of mortality in the intensive care unit. Early prediction of sepsis can reduce the overall mortality rate and cost of sepsis treatment. Some studies have predicted mortality and development of sepsis using machine learning models. However, there is a gap between the creation of different machine learning algorithms and their implementation in clinical practice. This study utilized data from the Medical Information Mart for Intensive Care III. We established and compared the gradient boosting decision tree (GBDT), logistic regression (LR), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM). A total of 3937 sepsis patients were included, with 34.3% mortality in the Medical Information Mart for Intensive Care III group. In our comparison of 5 machine learning models (GBDT, LR, KNN, RF, and SVM), the GBDT model showed the best performance with the highest area under the receiver operating characteristic curve (0.992), recall (94.8%), accuracy (95.4%), and F1 score (0.933). The RF, SVM, and KNN models showed better performance (area under the receiver operating characteristic curve: 0.980, 0.898, and 0.877, respectively) than the LR (0.876). The GBDT model showed better performance than other machine learning models (LR, KNN, RF, and SVM) in predicting the mortality of patients with sepsis in the intensive care unit. This could be used to develop a clinical decision support system in the future.

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“…And in order to take into account the retention of the original classification information, the adjustment is slow when processing new data. Typical algorithms include VFDT [20] , ODTC [21] , iCaRL [22] , and the online limit learning algorithm WOS-ELMK [23] , which using neural networks for learning, etc. The multi-classifier ensemble algorithm is more popular for the classification learning of accumulated big data where the data generation environment may change.…”

Section: The Related Research Work and Algorithmsmentioning

confidence: 99%

A Pruning Optimized Fast Learn++NSE Algorithm

et al. 2021

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Due to the large number of typical applications, it is very important and urgent to study the fast classification learning of accumulated big data in nonstationary environments. The newly proposed algorithm, named Learn++.NSE, is one of the important research results in this research field. And a pruning version, named Learn++.NSE-Error-based, was given for accumulated big data to improve the learning efficiency. However, the studies have found that the Learn++.NSE-Error-based algorithm often encounters a situation that the newly generated base classifier is pruned in the next integration, which reduces the accuracy of the ensemble classifier. The newly generated base classifier is very important in the next ensemble learning and should be retained. Therefore, the two latest base classifiers are retained without being pruned, and a new pruning algorithm named NewLearn++.NSE-Error-based was proposed. The experimental results on the generated dataset and the real-world dataset show that NewLearn++.NSE-Error-based can further improve the accuracy of the ensemble classifier under the premise of obtaining the same time complexity as Learn++.NSE algorithm. It is suitable for fast classification learning of long-term accumulated big data.

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An online software for decision tree classification and visualization using c4.5 algorithm (ODTC)

Cited by 8 publications

References 1 publication

Non-invasive monitoring with Ballistocardiographic sensors for sleep management

Non-invasive monitoring with Ballistocardiographic sensors for sleep management

Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree

A Pruning Optimized Fast Learn++NSE Algorithm

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