Adjusted F-measure and kernel scaling for imbalanced data learning

Maratea, Antonio; Petrosino, Alfredo; Manzo, Mario

doi:10.1016/j.ins.2013.04.016

Cited by 140 publications

(85 citation statements)

References 24 publications

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“…Table 1 lists the detection results. Table 2 lists the evaluation measures that are calculated by (12)- (15).…”

Section: Methodsmentioning

confidence: 99%

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Imbalanced SVM-Based Anomaly Detection Algorithm for Imbalanced Training Datasets

Wang¹,

Yang²,

Ren³

2017

ETRI Journal

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Abnormal samples are usually difficult to obtain in production systems, resulting in imbalanced training sample sets. Namely, the number of positive samples is far less than the number of negative samples. Traditional Support Vector Machine (SVM)-based anomaly detection algorithms perform poorly for highly imbalanced datasets: the learned classification hyperplane skews toward the positive samples, resulting in a high false-negative rate. This article proposes a new imbalanced SVM (termed ImSVM)-based anomaly detection algorithm, which assigns a different weight for each positive support vector in the decision function. ImSVM adjusts the learned classification hyperplane to make the decision function achieve a maximum GMean measure value on the dataset. The above problem is converted into an unconstrained optimization problem to search the optimal weight vector. Experiments are carried out on both Cloud datasets and Knowledge Discovery and Data Mining datasets to evaluate ImSVM. Highly imbalanced training sample sets are constructed. The experimental results show that ImSVM outperforms over-sampling techniques and several existing imbalanced SVM-based techniques.

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“…Table 1 lists the detection results. Table 2 lists the evaluation measures that are calculated by (12)- (15).…”

Section: Methodsmentioning

confidence: 99%

“…In addition, since the common evaluation measures (for example, false negative rate, false positive rate, and accuracy rate) are not applicable for evaluating the performance of anomaly detection algorithms on imbalanced datasets, some measures including F 1 -measure [15], GMean [11], [18] are introduced in the literature.…”

Section: Related Workmentioning

confidence: 99%

Imbalanced SVM-Based Anomaly Detection Algorithm for Imbalanced Training Datasets

Wang¹,

Yang²,

Ren³

2017

ETRI Journal

View full text Add to dashboard Cite

show abstract

“…For this an approximated hyperplane of the SVM was obtained and a kernel function was applied by computing the parameters through the chi-square test. Maratea et al improved the association capability of SVM by surpassing the approximate solution through kernel transformation [20]. The asymmetric space around the class boundary was enlarged that compensates the data skewness in binary classification.…”

Section: Related Workmentioning

confidence: 99%

An Ensemble Based Evolutionary Approach to the Class Imbalance Problem with Applications in CBIR

et al. 2018

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Abstract:In order to lower the dependence on textual annotations for image searches, the content based image retrieval (CBIR) has become a popular topic in computer vision. A wide range of CBIR applications consider classification techniques, such as artificial neural networks (ANN), support vector machines (SVM), etc. to understand the query image content to retrieve relevant output. However, in multi-class search environments, the retrieval results are far from optimal due to overlapping semantics amongst subjects of various classes. The classification through multiple classifiers generate better results, but as the number of negative examples increases due to highly correlated semantic classes, classification bias occurs towards the negative class, hence, the combination of the classifiers become even more unstable particularly in one-against-all classification scenarios. In order to resolve this issue, a genetic algorithm (GA) based classifier comity learning (GCCL) method is presented in this paper to generate stable classifiers by combining ANN with SVMs through asymmetric and symmetric bagging. The proposed approach resolves the classification disagreement amongst different classifiers and also resolves the class imbalance problem in CBIR. Once the stable classifiers are generated, the query image is presented to the trained model to understand the underlying semantic content of the query image for association with the precise semantic class. Afterwards, the feature similarity is computed within the obtained class to generate the semantic response of the system. The experiments reveal that the proposed method outperforms various state-of-the-art methods and significantly improves the image retrieval performance.

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“…4. Maratea et al (2014) defined Accuracy as "the probability of success in recognizing the right class of an instance. "…”

Section: Evaluation Phasementioning

confidence: 99%

Developing a prediction model for customer churn from electronic banking services using data mining

2016

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Background: Given the importance of customers as the most valuable assets of organizations, customer retention seems to be an essential, basic requirement for any organization. Banks are no exception to this rule. The competitive atmosphere within which electronic banking services are provided by different banks increases the necessity of customer retention. Methods: Being based on existing information technologies which allow one to collect data from organizations' databases, data mining introduces a powerful tool for the extraction of knowledge from huge amounts of data. In this research, the decision tree technique was applied to build a model incorporating this knowledge. Results:The results represent the characteristics of churned customers. Conclusions: Bank managers can identify churners in future using the results of decision tree. They should be provide some strategies for customers whose features are getting more likely to churner's features.

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Adjusted F-measure and kernel scaling for imbalanced data learning

Cited by 140 publications

References 24 publications

Imbalanced SVM-Based Anomaly Detection Algorithm for Imbalanced Training Datasets

Imbalanced SVM-Based Anomaly Detection Algorithm for Imbalanced Training Datasets

An Ensemble Based Evolutionary Approach to the Class Imbalance Problem with Applications in CBIR

Developing a prediction model for customer churn from electronic banking services using data mining

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