FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2495778891> ?p ?o ?g. }

• W2495778891 endingPage "640" @default.
• W2495778891 startingPage "611" @default.
• W2495778891 abstract "Class imbalance is one of the challenging problems for machine learning in many real-world applications. Many methods have been proposed to address and attempt to solve the problem, including sampling and cost-sensitive learning. The latter has attracted significant attention in recent years to solve the problem, but it is difficult to determine the precise misclassification costs in practice. There are also other factors that influence the performance of the classification including the input feature subset and the intrinsic parameters of the classifier. This paper presents an effective wrapper framework incorporating the evaluation measure (AUC and G-mean) into the objective function of cost sensitive learning directly for improve the performance of classification, by simultaneously optimizing the best pair of feature subset, intrinsic parameters and misclassification cost parameter. The optimization is based on Particle Swarm Optimization (PSO).We use two different common methods, support vector machine and feed forward neural networks to evaluate our proposed framework. Experimental results on various standard benchmark datasets with different ratios of imbalance and a real-world problem show that the proposed method is effective in comparison with commonly used sampling techniques. INTRODUCTION Recently, the class imbalance problem has been recognized as a crucial problem in machine learning and data mining (Chawla, Japkowicz K Kotsiantis, Kanellopoulos& Pintelas, 2006; He G He & Ma, 2013). This issue of imbalanced data occurs when the training data is not evenly distributed among classes. This problem is also especially critical in many real applications, such as credit card fraud detection when fraudulent cases are rare or medical diagnoses where normal cases are the majority, and it is growing in importance and has been identified as one of the 10 main challenges of Data Mining (Yang, 2006). In these cases, standard classifiers generally perform poorly. Classifiers usually tend to be overwhelmed by the majority class and ignore the minority class examples. Most classifiers assume an even distribution of examples among classes and assume an equal misclassification cost. Moreover, classifiers are typically designed to maximize accuracy, which is not a good metric to evaluate effectiveness in the case of imbalanced training data. Therefore, we need to improve traditional algorithms so as to handle imbalanced data and choose other metrics to measure performance instead of accuracy. We focus our study on imbalanced datasets with binary classes. Much work has been done in addressing the class imbalance problem. These methods can be grouped in two categories: the data perspective and the algorithm perspective (He &Garcia 2009). The methods with the data perspective re-balance the class distribution by re-sampling the data space either" @default.
• W2495778891 created "2016-08-23" @default.
• W2495778891 creator A5053745515 @default.
• W2495778891 creator A5054322583 @default.
• W2495778891 creator A5085833803 @default.
• W2495778891 date "2017-02-07" @default.
• W2495778891 modified "2023-10-18" @default.
• W2495778891 title "A Measure Optimized Cost-Sensitive Learning Framework for Imbalanced Data Classification" @default.
• W2495778891 cites W1506588750 @default.
• W2495778891 cites W1543436687 @default.
• W2495778891 cites W1544435011 @default.
• W2495778891 cites W1551909886 @default.
• W2495778891 cites W1563938718 @default.
• W2495778891 cites W1572482543 @default.
• W2495778891 cites W169052826 @default.
• W2495778891 cites W197274371 @default.
• W2495778891 cites W1976790167 @default.
• W2495778891 cites W1977870906 @default.
• W2495778891 cites W2023450550 @default.
• W2495778891 cites W2032867948 @default.
• W2495778891 cites W2053724458 @default.
• W2495778891 cites W2058514166 @default.
• W2495778891 cites W2070808135 @default.
• W2495778891 cites W2081423733 @default.
• W2495778891 cites W2087240369 @default.
• W2495778891 cites W2093604575 @default.
• W2495778891 cites W2106479238 @default.
• W2495778891 cites W2109943925 @default.
• W2495778891 cites W2110504884 @default.
• W2495778891 cites W2118978333 @default.
• W2495778891 cites W2119191234 @default.
• W2495778891 cites W2119498311 @default.
• W2495778891 cites W2120040939 @default.
• W2495778891 cites W2123458540 @default.
• W2495778891 cites W2123977051 @default.
• W2495778891 cites W2125314042 @default.
• W2495778891 cites W2127023476 @default.
• W2495778891 cites W2131391419 @default.
• W2495778891 cites W2139393465 @default.
• W2495778891 cites W2148143831 @default.
• W2495778891 cites W2152195021 @default.
• W2495778891 cites W2152464310 @default.
• W2495778891 cites W2163563074 @default.
• W2495778891 cites W2163735391 @default.
• W2495778891 cites W2166249949 @default.
• W2495778891 cites W2168025622 @default.
• W2495778891 cites W218853445 @default.
• W2495778891 cites W2320438893 @default.
• W2495778891 cites W2404077489 @default.
• W2495778891 cites W571200655 @default.
• W2495778891 cites W69249895 @default.
• W2495778891 cites W85350352 @default.
• W2495778891 cites W3022291425 @default.
• W2495778891 doi "https://doi.org/10.4018/978-1-5225-1759-7.ch026" @default.
• W2495778891 hasPublicationYear "2017" @default.
• W2495778891 type Work @default.
• W2495778891 sameAs 2495778891 @default.
• W2495778891 citedByCount "2" @default.
• W2495778891 countsByYear W24957788912015 @default.
• W2495778891 countsByYear W24957788912020 @default.
• W2495778891 crossrefType "book-chapter" @default.
• W2495778891 hasAuthorship W2495778891A5053745515 @default.
• W2495778891 hasAuthorship W2495778891A5054322583 @default.
• W2495778891 hasAuthorship W2495778891A5085833803 @default.
• W2495778891 hasConcept C119857082 @default.
• W2495778891 hasConcept C12267149 @default.
• W2495778891 hasConcept C124101348 @default.
• W2495778891 hasConcept C13280743 @default.
• W2495778891 hasConcept C138885662 @default.
• W2495778891 hasConcept C154945302 @default.
• W2495778891 hasConcept C185798385 @default.
• W2495778891 hasConcept C205649164 @default.
• W2495778891 hasConcept C2776401178 @default.
• W2495778891 hasConcept C41008148 @default.
• W2495778891 hasConcept C41895202 @default.
• W2495778891 hasConcept C85617194 @default.
• W2495778891 hasConcept C95623464 @default.
• W2495778891 hasConceptScore W2495778891C119857082 @default.
• W2495778891 hasConceptScore W2495778891C12267149 @default.
• W2495778891 hasConceptScore W2495778891C124101348 @default.
• W2495778891 hasConceptScore W2495778891C13280743 @default.
• W2495778891 hasConceptScore W2495778891C138885662 @default.
• W2495778891 hasConceptScore W2495778891C154945302 @default.
• W2495778891 hasConceptScore W2495778891C185798385 @default.
• W2495778891 hasConceptScore W2495778891C205649164 @default.
• W2495778891 hasConceptScore W2495778891C2776401178 @default.
• W2495778891 hasConceptScore W2495778891C41008148 @default.
• W2495778891 hasConceptScore W2495778891C41895202 @default.
• W2495778891 hasConceptScore W2495778891C85617194 @default.
• W2495778891 hasConceptScore W2495778891C95623464 @default.
• W2495778891 hasLocation W24957788911 @default.
• W2495778891 hasOpenAccess W2495778891 @default.
• W2495778891 hasPrimaryLocation W24957788911 @default.
• W2495778891 hasRelatedWork W1753659477 @default.
• W2495778891 hasRelatedWork W2075267337 @default.
• W2495778891 hasRelatedWork W2088059023 @default.
• W2495778891 hasRelatedWork W2617368871 @default.
• W2495778891 hasRelatedWork W2622167309 @default.

• next