Analysis of traffic accident severity using decision rules via decision trees
A Decision Tree (DT) is a potential method for studying traffic accident severity. One of its
main advantages is that Decision Rules (DRs) can be extracted from its structure. And these …
main advantages is that Decision Rules (DRs) can be extracted from its structure. And these …
Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring
Previous studies about ensembles of classifiers for bankruptcy prediction and credit scoring
have been presented. In these studies, different ensemble schemes for complex classifiers …
have been presented. In these studies, different ensemble schemes for complex classifiers …
Credal-C4. 5: Decision tree based on imprecise probabilities to classify noisy data
In the area of classification, C4. 5 is a known algorithm widely used to design decision trees.
In this algorithm, a pruning process is carried out to solve the problem of the over-fitting. A …
In this algorithm, a pruning process is carried out to solve the problem of the over-fitting. A …
Nonparametric predictive inference for system reliability using the survival signature
FPA Coolen, T Coolen-Maturi… - Proceedings of the …, 2014 - journals.sagepub.com
The survival signature has recently been presented as an attractive concept to aid
quantification of system reliability. It has similar characteristics as the system signature …
quantification of system reliability. It has similar characteristics as the system signature …
A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov–Smirnov bounds
MS Kovalev, LV Utkin - Neural Networks, 2020 - Elsevier
A new robust algorithm based on the explanation method SurvLIME called SurvLIME-KS is
proposed for explaining machine learning survival models. The algorithm is developed to …
proposed for explaining machine learning survival models. The algorithm is developed to …
A data envelopment analysis (DEA)-based method for rule reduction in extended belief-rule-based systems
Rule reduction is one of the research objectives in numerous successful rule-based
systems. In some analyses, too many useless rules may be a concern in a rule-based …
systems. In some analyses, too many useless rules may be a concern in a rule-based …
Automated classification between age-related macular degeneration and diabetic macular edema in OCT image using image segmentation
J Sugmk, S Kiattisin… - The 7th 2014 biomedical …, 2014 - ieeexplore.ieee.org
Age-related macular degeneration (AMD) and Diabetic macular edema (DME) are to lead
causes to make a visual loss in people. People are suffered from the use of many time to …
causes to make a visual loss in people. People are suffered from the use of many time to …
Imprecise weighted extensions of random forests for classification and regression
One of the main problems of using the random forests (RF) in classification and regression
tasks is a lack of sufficient data which fall into certain leaves of trees in order to estimate the …
tasks is a lack of sufficient data which fall into certain leaves of trees in order to estimate the …
[PDF][PDF] Nonparametric Predictive Inference.
FPA Coolen - 2011 - tahanimaturi.com
Nonparametric Predictive Inference (NPI) is a statistical methodology based on Hill's
assumption A (n)[30], which gives a direct conditional probability for a future observable …
assumption A (n)[30], which gives a direct conditional probability for a future observable …
A framework for extended belief rule base reduction and training with the greedy strategy and parameter learning
The extended belief rule-based system has been used in the field of decision making in
recent years for its advantage of expressing various kinds of information under uncertainty …
recent years for its advantage of expressing various kinds of information under uncertainty …