The Comparison of Gini and Twoing Algorithms in Terms of Predictive Ability and Misclassification Cost in Data Mining: An Empirical Study

Kayrı, Murat

doi:10.14445/22312803/ijctt-v27p105

Cited by 6 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While the LDT leaf size was directly optimized (∼200), because a larger number of node (or splits) in the tree implies a larger memory occupation (data latency is not an issue, being proportional simply to the logarithm of the tree size), the maximum number of splits was set equal to the minimum value that allowed accuracy optimization within 1% from the absolute optimum, (∼3×10 4 ). Twoing rule [58] was selected as splitting criterion for both GDT and LDTs.…”

Section: Decision Trees: Experimental Resultsmentioning

confidence: 99%

Experimental Assessment of PCA and DT Classification for Streamlined Position Reconstruction in Anger Cameras

Pedretti

Giacomo

Buonanno

et al. 2023

IEEE Trans. Radiat. Plasma Med. Sci.

View full text Add to dashboard Cite

In this paper we present a study of machine learning (ML) algorithms to simplify the computation of the planar scintillation coordinates in Anger Cameras for emission tomography applications. Two ML-based techniques for data inference and one technique for speed-up the training procedure are explored within the framework of a multimodal SPECT scanner. Firstly, the use of Principal Component Analysis (PCA), a dimensionality reduction algorithm, is explored to reduce the computational complexity of maximum-likelihood statistical estimation method. The analysis indicates a ∼3-fold reduction of computational complexity for typical Anger Camera architectures (with 72 channels). Secondly, the estimation of the scintillation coordinates is formulated as a classification problem, addressed by means of a Decision Tree (DT) classifier. No degradation of the achievable intrinsic spatial resolution (∼1.2 mm FWHM) of the detection module was observed when applying PCA (reducing from 72 to 25 components). The DT classifier was trained on experimental data obtained using a parallel-hole collimator: again no degradation of spatial resolution is observed and the computation cost is reduced by more than two orders of magnitude. Finally, in order to overcome the limits of a cumbersome training procedure involving the translation of the collimator, data augmentation was successfully leveraged for the generation of artificial data.

show abstract

Section: Decision Trees: Experimental Resultsmentioning

confidence: 99%

Experimental Assessment of PCA and DT Classification for Streamlined Position Reconstruction in Anger Cameras

Pedretti

Giacomo

Buonanno

et al. 2023

IEEE Trans. Radiat. Plasma Med. Sci.

View full text Add to dashboard Cite

show abstract

“…Table 2 contains training model details, along with visualizations for evaluating performance. A separate study was conducted to test the Optimizable Tree method for maximum deviance reduction [38], Towing Rule [39], and Gini's Diversity Index [40]. It was tried to examine how many splits there are from 1 to 560.…”

Section: Pathological Data-based Machine Learning Model Developmentmentioning

confidence: 99%

COVED: A Hardware Accelerated Soft Computing Enabled Intelligent Value Chain Based Diagnostic Automation for nCOVID-19 Estimation and Identification

Biswas¹,

Sen

Bhatia

et al. 2021

ijsmr

View full text Add to dashboard Cite

Purpose: COVID-19, a global pandemic, first appeared in the city of Wuhan, China, and has since spread differently across geographical borders, classes, and genders from various age groups, sometimes mutating its DNA strands in the process. The sheer magnitude of the pandemic's spread is putting a strain on hospitals and medical facilities. The need of the hour is to deploy IoT devices and robots to monitor patients' body vitals as well as their other pathological data to further control the spread. There has not been a more compelling need to use digital advances to remotely provide quality healthcare via computing devices and AI-powered medical aids. Method: This research developed a deployable Internet of Things (IoT) based infrastructure for the early and simple detection and isolation of suspected coronavirus patients, which was accomplished via the use of ensemble deep transfer learning. The proposed Internet of Things framework combines 4 different deep learning models: DenseNet201, VGG16, InceptionResNetV2, and ResNet152V2. Utilizing the deep ensemble model, the medical modalities are used to obtain chest high-resolution computed tomography (HRCT) images and diagnose the infection. Results: Over the HRCT image dataset, the developed deep ensemble model is collated to different state-of-the-art transfer learning (TL) models. The comparative investigation demonstrated that the suggested approach can aid radiologists inefficiently and swiftly diagnosing probable coronavirus patients. Conclusion: For the first time, our group has developed an AI-enabled Decision Support System to automate the entire process flow from estimation to detection of COVID-19 subjects as part of an Intelligent Value Chain algorithm. The screening is expected to eliminate the false negatives and asymptomatic ones out of the equation and hence the affected individuals could be identified in a total process time of 15 minutes to 1 hour. A Complete Deployable System with AI Influenced Prediction is described here for the first time. Not only did the authors suggest a Multiple Hypothesis based Decision Fusion Algorithm for forecasting the outcome, but they also did the predictive analytics. For simple confined isolation or hospitalization, this complete Predictive System was encased within an IoT ecosystem.

show abstract

“…In case of regression, C&RT looks for splits that minimize the prediction squared error (the least squared deviation). The prediction in each leaf is based on the weighted mean for node [10,33,51,62, 64].…”

Section: Brief Description Of the Statistical Model Structuresmentioning

confidence: 99%

Prediction of tunnel wall convergences for NATM tunnels which are excavated in weak-to-fair-quality rock masses using decision-tree technique and rock mass strength parameters

Satıcı¹,

Topal

2020

SN Appl. Sci.

View full text Add to dashboard Cite

Tunnel wall convergences should be predicted before the excavation and should be determined accurately in order to ensure safe and economic tunnel excavation media. For this to be possible, rock mass behaviors in the tunnel should also be estimated prior to excavation. This could be ensured by site investigation studies, numerical models and predictive statistics. In this study, the development of convergences during a tunnel excavation in Turkey, excavated by NATM technique, was evaluated by using statistical prediction techniques for weak-to-fair-quality rock masses. For this aim, actual tunnel wall convergence data and rock mass strength parameters were used. For prediction of tunnel wall convergence, multivariable regression analysis, artificial neural networks (ANNs), classification and regression tree (CHAID) and Chisquare automatic interaction detection (C&RT) methods were used and prediction results were compared to each other in terms of superiority and practicality. For this aim, 112 tunnel sections were used for prediction model and 30 different tunnel sections were used for validation. According to the obtained statistical prediction findings, it is seen that C&RT and ANN methods provide good prediction of tunnel wall convergences. However, C&RT method was found more practical in field use when compared to ANN. The results have shown that overburden thickness is the most effective parameter on tunnel convergence when compared to C rm , Φ rm , E rm , RMR and Q. However, the best way for determination of tunnel wall convergences is to use in situ measurements, but in case of the lack of in situ measurement instruments, the suggested probability-based statistical approach is proven to be very effective and practical. Ultimate convergence level for any cross section with similar geological and geotechnical parameters to those analyzed before can be predicted by using the suggested method in this study. Yet, it should be kept in mind that the findings of this study are limited with the data used in this study. Although the aim of this study is to ensure a different point of view for determination of convergences in case of lack of convergence measurements and field data, by adding up more convergence measurements and rock mass strength data to the proposed statistical method, prediction power of this method can be improved and then this method can be used as a practical tool for the prediction of tunnel convergences. Besides, the user-friendly and opento-development structure of this study can be a very useful tool for the geotechnical engineers, engineering geologist and mining engineers, if it can be developed by more field data.

show abstract

The Comparison of Gini and Twoing Algorithms in Terms of Predictive Ability and Misclassification Cost in Data Mining: An Empirical Study

Cited by 6 publications

References 30 publications

Experimental Assessment of PCA and DT Classification for Streamlined Position Reconstruction in Anger Cameras

Experimental Assessment of PCA and DT Classification for Streamlined Position Reconstruction in Anger Cameras

COVED: A Hardware Accelerated Soft Computing Enabled Intelligent Value Chain Based Diagnostic Automation for nCOVID-19 Estimation and Identification

Prediction of tunnel wall convergences for NATM tunnels which are excavated in weak-to-fair-quality rock masses using decision-tree technique and rock mass strength parameters

Contact Info

Product

Resources

About