Genetic programming for development of cost-sensitive classifiers for binary high-dimensional unbalanced classification

Pei, Wenbin; Xue, Bing; Shang, Lin; Zhang, Mengjie

doi:10.1016/j.asoc.2020.106989

Cited by 24 publications

(7 citation statements)

References 12 publications

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“…Te performance of the feature subset is measured by a classifer. Tus, we adopt the confusion matrix [47] as an evaluation metric, and the ftness is the error rate, which is updated by Equation (19).…”

Section: Dynamic Learningmentioning

confidence: 99%

Semisupervised Bacterial Heuristic Feature Selection Algorithm for High‐Dimensional Classification with Missing Labels

Wang

et al. 2023

International Journal of Intelligent Systems

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Feature selection is a crucial method for discovering relevant features in high-dimensional data. However, most studies primarily focus on completely labeled data, ignoring the frequent occurrence of missing labels in real-world problems. To address high-dimensional and label-missing problems in data classification simultaneously, we proposed a semisupervised bacterial heuristic feature selection algorithm. To track the label-missing problem, a k-nearest neighbor semisupervised learning strategy is designed to reconstruct missing labels. In addition, the bacterial heuristic algorithm is improved using hierarchical population initialization, dynamic learning, and elite population evolution strategies to enhance the search capacity for various feature combinations. To verify the effectiveness of the proposed algorithm, three groups of comparison experiments based on eight datasets are employed, including two traditional feature selection methods, four bacterial heuristic feature selection algorithms, and two swarm-based heuristic feature selection algorithms. Experimental results demonstrate that the proposed algorithm has obvious advantages in terms of classification accuracy and selected feature numbers.

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Section: Dynamic Learningmentioning

confidence: 99%

Semisupervised Bacterial Heuristic Feature Selection Algorithm for High‐Dimensional Classification with Missing Labels

Wang

et al. 2023

International Journal of Intelligent Systems

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“…As seen in the previous section, this problem is even more evident when using sliding windows. As a result, the most common classifiers, those based on maximizing the classification accuracy, either tend to over-fit the minority class or completely ignore it [21], [22].…”

Section: B Minimum Recall Based Function To Deal With the Imbalanced ...mentioning

confidence: 99%

A Multivariate Time Series Streaming Classifier for Predicting Hard Drive Failures [Application Notes]

Ircio

Lojo

Lozano

et al. 2022

IEEE Comput. Intell. Mag.

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Digital data storage systems such as hard drives can suffer breakdowns that cause the loss of stored data. Due to the cost of data and the damage that its loss entails, hard drive failure prediction is vital. In this context, the objective of this paper is to develop a method for detecting the beginning of hard drive malfunction using streaming SMART data, allowing the user to take actions before the breakdown occurs. This is a challenging task for two main reasons. First, there are not usually many examples of failed hard drives. Second, in these few available examples, hard drives are only identified and labeled as failed after complete breakdown occurs, but the exact moment when they begin to malfunction is usually unknown. Both these aspects significantly complicate the supervised learning of hard drive failure prediction models. To cope with these issues, the problem is addressed as a multidimensional time series streaming classification problem based on sliding windows. Moreover, as a solution to the highly imbalanced situation, the learned classifier is optimized to maximize the minimum recall of classes. Experimental results using the Backblaze benchmark dataset show that the proposed method reliably anticipates hard drive failures and obtains a higher balance between the recall values of both classes, failed and correct disks, compared to other state-of-the-art solutions.

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“…The particles of each subgroup are divided into ordinary particles, and local optimal particles based on the result of the division of subgroups. Under the primary guidance of the optimal particles, the ordinary particles exert their local search ability, and the updated formula is given as (7).…”

Section: Apso-rf Unbalanced Data Classification Modelmentioning

confidence: 99%

“…Cost-sensitive learning method [7] assigns different values to the misclassification costs of different categories, generally, the minority in the categories are expensive, and the cost of majority is low; the approach of a cost-sensitive classifier is to handle the problems with different error costs. It also might end up with over-specific rules.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Optimization Swarm Algorithm Ensemble Model Applied to the Classification of Unbalanced Data

He¹,

Qin²

2021

IIM

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In order to solve the problem that the hyper-parameters of the existing random forest-based classification prediction model depend on empirical settings, which leads to unsatisfactory model performance. We propose a based on adaptive particle swarm optimization algorithm random forest model to optimize data classification and an adaptive particle swarm algorithm for optimizing hyper-parameters in the random forest to ensure that the model can better predict unbalanced data. Aiming at the premature convergence problem in the particle swarm optimization algorithm, the population is adaptively divided according to the fitness of the population, and an adaptive update strategy is introduced to enhance the ability of particles to jump out of the local optimum. The main steps of the model are as follows: Normalize the data set, initialize the model on the training set, and then use the particle swarm optimization algorithm to optimize the modeling process to establish a classification model. Experimental results show that our proposed algorithm is better than traditional algorithms, especially in terms of F1-Measure and ACC evaluation standards. The results of the six-keel imbalanced data set demonstrate the advantages of our proposed algorithm.

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Genetic programming for development of cost-sensitive classifiers for binary high-dimensional unbalanced classification

Cited by 24 publications

References 12 publications

Semisupervised Bacterial Heuristic Feature Selection Algorithm for High‐Dimensional Classification with Missing Labels

Semisupervised Bacterial Heuristic Feature Selection Algorithm for High‐Dimensional Classification with Missing Labels

A Multivariate Time Series Streaming Classifier for Predicting Hard Drive Failures [Application Notes]

Adaptive Optimization Swarm Algorithm Ensemble Model Applied to the Classification of Unbalanced Data

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