Time-constrained cost-sensitive decision tree induction

Chen, Yen-Liang; Wu, Ching‐Yuan; Tang, Kwei

doi:10.1016/j.ins.2016.03.022

Cited by 22 publications

(8 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The test cost and test time for the variables in both the Pima Indians Diabetes and the Car datasets are set randomly between 1 and 100, as shown in Tables 4 and 5, respectively. The time constraint maxtime is estimated by the method described in Chen et al [5] and set as 300, 349, and 150 for the Heart Disease, diabetes, and Car data, respectively.…”

Section: Datasetsmentioning

confidence: 99%

“…Wan [48] proposes a multi-batch strategy. Kao and Tang [17] introduce the cost-sensitive-with-late-constraint (CSLC), and Chen, Wu, and Tang [5] present a time-constrained minimal-cost decision tree (henceforth, CWT algorithm), showing satisfactory performance. Wu, Chen, and Tang [50] extend CWT with the Cost-Sensitive Associative Tree (CAT) for multiple resource constraints.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cost‐sensitive classification with time constraint on incomplete data

Lee,

2024

Statistical Analysis

View full text Add to dashboard Cite

Missing values are common, but dealing with them by inappropriate method may lead to large classification errors. Empirical evidences show that the tree‐based classification algorithms such as classification and regression tree (CART) can benefit from imputation, especially multiple imputation. Nevertheless, less attention has been paid to incorporating multiple imputation into cost‐sensitive decision tree induction. This study focuses on the treatment of missing data based on a time‐constrained minimal‐cost tree algorithm. We introduce various approaches to handle incomplete data into the algorithm including complete‐case analysis, missing‐value branch, single imputation, feature acquisition, and multiple imputation. A simulation study under different scenarios examines the predictive performances of the proposed strategies. The simulation results show that the combination of the algorithm with multiple imputation can assure classification accuracy under the budget. A real medical data example provides insights into the problem of missing values in cost‐sensitive learning and the advantages of the proposed methods.

show abstract

Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cost‐sensitive classification with time constraint on incomplete data

Lee,

2024

Statistical Analysis

View full text Add to dashboard Cite

show abstract

“…Because the coefficients in the high-frequency wavelet subbands are sparse, we can divide the subbands into several blocks and then check whether they contain significant coefficients. The tree structure is widely used in many fields of information sciences [30,31,32], and is also a good model for compression [33]. In [7], each block is split into four sub-blocks once it tests as significant with respective to the current threshold.…”

Section: The Btca Algorithmmentioning

confidence: 99%

Remote sensing image compression based on binary tree and optimized truncation

Huang

Ren

et al. 2017

Digital Signal Processing

View full text Add to dashboard Cite

The remote sensing image data is so vast that it requires compression by low-complexity algorithm on space-borne equipment. Binary tree coding with adaptive scanning order (BTCA) is an effective algorithm for the mission. However, for large-scale remote sensing images, BTCA requires a lot of memory, and does not provide random access property. In this paper, we propose a new coding method based on BTCA and optimize truncation. The wavelet image is first divided into several blocks which are encoded individually by BTCA. According the property of BTCA, we select the valid truncation points for each block carefully to optimize the ratio of rate-distortion, so that a higher compression ratio, lower memory requirement and random access property are attained. Without any entropy coding, the proposed method is simple and fast, which is very suitable for space-borne equipment. Experiments are conducted on three remote sensing image sets, and the results show that it can significantly improve PSNR, SSIM and VIF, as well as subjective visual experience.

show abstract

“…In other words, when there is enough time to build a tree, the algorithm should choose the feature to split having the most benefit while it chooses the most effective feature in terms of time when the time is limited. However, the method has not been implemented for big data with respect to the complexity of the resulting tree [3].…”

Section: Introductionmentioning

confidence: 99%

The Construction of Scalable Decision Tree based on Fast Splitting and J-Max Pre-Pruning on Large Datasets

Lotfi

Ghasemzadeh

Mohsenzadeh

et al. 2021

IJE

View full text Add to dashboard Cite

The decision tree is one of the most important algorithms in the classification which offers a comprehensible model of data. In building a tree we may encounter a memory limitation. The present study aims to implement an incremental scalable approach based on fast splitting, and employs a pruning technique to construct the decision tree on a large dataset to reduce the complexity of the tree. The proposed algorithm constructs the decision tree without storing the entire dataset in the primary memory via employing a minimum number of parameters. Furthermore, the J-max Pre pruning method was used to reduce the complexity with acceptable results. Experimental results show that this approach can create a balance between the accuracy and complexity of the tree and overcome the difficulties of the complexity of the tree. In spite of the appropriate accuracy and time, the proposed algorithm could produce a decision tree with less complexity on a large dataset.

show abstract

Time-constrained cost-sensitive decision tree induction

Cited by 22 publications

References 23 publications

Cost‐sensitive classification with time constraint on incomplete data

Cost‐sensitive classification with time constraint on incomplete data

Remote sensing image compression based on binary tree and optimized truncation

The Construction of Scalable Decision Tree based on Fast Splitting and J-Max Pre-Pruning on Large Datasets

Contact Info

Product

Resources

About