New Applications of Ensembles of Classifiers

Barandela, Ricardo; Valdovinos, R. M.; Sánchez, José

doi:10.1007/s10044-003-0192-z

Cited by 252 publications

(142 citation statements)

References 35 publications

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“…The MCSs have been integrated by using four well-known resampling methods: random selection with no replacement [1], bagging [2], boosting [8], and Arc-x4 [3]. Only the result of the best technique on each database has been presented in Table 1.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Combining Multiple Classifiers with Dynamic Weighted Voting

Valdovinos¹,

Sánchez

2009

Lecture Notes in Computer Science

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Abstract. When a multiple classifier system is employed, one of the most popular methods to accomplish the classifier fusion is the simple majority voting. However, when the performance of the ensemble members is not uniform, the efficiency of this type of voting generally results affected negatively. In the present paper, new functions for dynamic weighting in classifier fusion are introduced. Experimental results with several real-problem data sets from the UCI Machine Learning Database Repository demonstrate the advantages of these novel weighting strategies over the simple voting scheme.

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Section: Experiments and Resultsmentioning

confidence: 99%

Combining Multiple Classifiers with Dynamic Weighted Voting

Valdovinos¹,

Sánchez

2009

Lecture Notes in Computer Science

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“…Previous works demonstrated that a MES trained on balanced subsets of the original TS better recognises minority class patterns than IC [10,11,12], as discussed in section 2. Based on these observations, the proposed method works as follows.…”

Section: Methodsmentioning

confidence: 99%

“…The last approach coping with imbalanced TS is based on multi-experts system, also known as ensemble of classifiers, where each composing classifier is trained on a subset of the majority class and on the whole minority class [10,11,12]. The idea is based on the widely accepted result that a MES approach generally produces better results than those obtained by individual composing experts, since different features and recognition systems complement each other in classification performance.…”

Section: Techniques For Handling Imbalancedmentioning

confidence: 99%

“…In [10] the authors generated as many training subsets as required to get balanced subsets from the given TS. The number of subsets was determined by the difference between the number of samples in majority and minority classes.…”

Section: Techniques For Handling Imbalancedmentioning

confidence: 99%

“…Despite several existing proposals [1,2,3,4,5,6,7,8,9,10,11,12], some issues remained opened. For instance, balancing the recognition accuracies achieved for each class very often decreases the global accuracy.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Approach Handling Imbalanced Datasets

Soda

2009

Image Analysis and Processing – ICIAP 2009

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Abstract. Several binary classification problems exhibit imbalance in class distribution, influencing system learning. Indeed, traditional machine learning algorithms are biased towards the majority class, thus producing poor predictive accuracy over the minority one. To overcome this limitation, many approaches have been proposed up to now to build artificially balanced training sets. Further to their specific drawbacks, they achieve more balanced accuracies on each class harming the global accuracy. This paper first reviews the more recent method coping with imbalanced datasets and then proposes a strategy overcoming the main drawbacks of existing approaches. It is based on an ensemble of classifiers trained on balanced subsets of the original imbalanced training set working in conjunction with the classifier trained on the original imbalanced dataset. The performance of the method has been estimated on six public datasets, proving its effectiveness also in comparison with other approaches. It also gives the chance to modify the system behaviour according to the operating scenario.

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Building an intrusion detection system on UNSW‐NB15: Reducing the margin of error to deal with data overlap and imbalance

Zoghi,

Serpen

2024

Concurrency and Computation

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SummaryThis study addresses the challenge of data imbalance and class overlap in machine learning for intrusion detection, proposing that targeted algorithmic adjustments can significantly enhance model performance. Our hypothesis contends that an ensemble framework, adeptly integrating novel threshold‐adjustment algorithms, can improve classification sensitivity and specificity. To test this, we developed an ensemble model comprising Balanced Bagging (BB), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF), fine‐tuned using grid search for BB and XGBoost, and augmented with the Hellinger metric for RF to tackle data imbalance. The innovation lies in our algorithms, which adeptly adjust the discrimination threshold to rectify the class overlap problem, enhancing the model's ability to discern between negative and positive classes. Utilizing the UNSW‐NB15 dataset, we conducted a comparative analysis for binary and multi‐category classification. Our ensemble model achieved a binary classification accuracy of 97.80%, with a sensitivity rate of 98.26% for detecting attacks, and a multi‐category classification accuracy and sensitivity that reached up to 99.73% and 97.24% for certain attack types. These results substantially surpass those of existing models on the same dataset, affirming our model's superiority in dealing with complex data distributions prevalent in network security domains.

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New Applications of Ensembles of Classifiers

Cited by 252 publications

References 35 publications

Combining Multiple Classifiers with Dynamic Weighted Voting

Combining Multiple Classifiers with Dynamic Weighted Voting

A Hybrid Approach Handling Imbalanced Datasets

Building an intrusion detection system on UNSW‐NB15: Reducing the margin of error to deal with data overlap and imbalance

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