Recent advances in decision trees: An updated survey
VG Costa, CE Pedreira - Artificial Intelligence Review, 2023 - Springer
Abstract Decision Trees (DTs) are predictive models in supervised learning, known not only
for their unquestionable utility in a wide range of applications but also for their interpretability …
for their unquestionable utility in a wide range of applications but also for their interpretability …
A cluster-based oversampling algorithm combining SMOTE and k-means for imbalanced medical data
The algorithm of C4. 5 decision tree has the advantages of high classification accuracy, fast
calculation speed and comprehensible classification rules, so it is widely used for medical …
calculation speed and comprehensible classification rules, so it is widely used for medical …
Improved swarm-optimization-based filter-wrapper gene selection from microarray data for gene expression tumor classification
L Ke, M Li, L Wang, S Deng, J Ye, X Yu - Pattern Analysis and Applications, 2023 - Springer
A typical microarray dataset usually contains thousands of genes, but only a small number of
samples. It is in fact that most genes in a DNA microarray dataset are not relevant for …
samples. It is in fact that most genes in a DNA microarray dataset are not relevant for …
An adaptive Laplacian weight random forest imputation for imbalance and mixed-type data
As the application of information technology in the medical field is resulting in a large
amount of medical data. As early withdrawal and refusal of participants, there are a lot of …
amount of medical data. As early withdrawal and refusal of participants, there are a lot of …
Targeting customers for profit: An ensemble learning framework to support marketing decision-making
Marketing messages are most effective if they reach the right customers. Deciding which
customers to contact is an important task in campaign planning. The paper focuses on …
customers to contact is an important task in campaign planning. The paper focuses on …
A new oversampling method and improved radial basis function classifier for customer consumption behavior prediction
In practical applications, imbalanced data has brought great challenges to classification
problems. In this paper, we propose two new methods:(1) a new oversampling method …
problems. In this paper, we propose two new methods:(1) a new oversampling method …
An assertive reasoning method for emergency response management based on knowledge elements C4. 5 decision tree
L Han, W Li, Z Su - Expert Systems with Applications, 2019 - Elsevier
The correct selection of knowledge elements is the key to emergency management. Using
emergency knowledge elements, this study constructs an assertive reasoning selection …
emergency knowledge elements, this study constructs an assertive reasoning selection …
An adaptive machine learning algorithm for the resource-constrained classification problem
Resource-constrained classification tasks are common in real-world applications such as
allocating tests for disease diagnosis, hiring decisions when filling a limited number of …
allocating tests for disease diagnosis, hiring decisions when filling a limited number of …
Learning misclassification costs for imbalanced classification on gene expression data
Background Cost-sensitive algorithm is an effective strategy to solve imbalanced
classification problem. However, the misclassification costs are usually determined …
classification problem. However, the misclassification costs are usually determined …
Developing interval-based cost-sensitive classifiers by genetic programming for binary high-dimensional unbalanced classification [research frontier]
Cost-sensitive learning is a popular approach to addressing the problem of class imbalance
for many classification algorithms in machine learning. However, most cost-sensitive …
for many classification algorithms in machine learning. However, most cost-sensitive …