Journal ArticleDOI
Handling Class Overlapping to Detect Noisy Instances in Classification
Shivani Gupta,Atul Gupta +1 more
TLDR
It is found that class overlap is a principal contributor to introduce class noise in data sets by using four noise filters to identify the overlapped instances and find the noisy instances.Abstract:
Automated machine classification will play a vital role in the machine learning and data mining. It is probable that each classifier will work well on some data sets and not so well in others, increasing the evaluation significance. The performance of the learning models will intensely rely on upon the characteristics of the data sets. The previous outcomes recommend that overlapping between classes and the presence of noise has the most grounded impact on the performance of learning algorithm. The class overlap problem is a critical problem in which data samples appear as valid instances of more than one class which may be responsible for the presence of noise in data sets.The objective of this paper is to comprehend better the data used as a part of machine learning problems so as to learn issues and to analyze the instances that are profoundly covered by utilizing new proposed overlap measures. The proposed overlap measures are Nearest Enemy Ratio, SubConcept Ratio, Likelihood Ratio and Soft Margin Ratio. To perform this experiment, we have created 438 binary classification data sets from real-world problems and computed the value of 12 data complexity metrics to find highly overlapped data sets. After that we apply measures to identify the overlapped instances and four noise filters to find the noisy instances. From results, we found that 60–80% overlapped instances are noisy instances in data sets by using four noise filters. We found that class overlap is a principal contributor to introduce class noise in data sets.read more
Citations
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Journal ArticleDOI
A novel progressively undersampling method based on the density peaks sequence for imbalanced data
TL;DR: Zhang et al. as discussed by the authors proposed a novel under-sampling method for imbalanced data, which exploits a sequence of density peaks to progressively extract instances from the majority classes of the imbalance data.
Journal ArticleDOI
A New Under-Sampling Method to Face Class Overlap and Imbalance
TL;DR: A two-stage under-sampling technique that combines the DBSCAN clustering algorithm to remove noisy samples and clean the decision boundary with a minimum spanning tree algorithm to face the class imbalance, thus handling class overlap and imbalance simultaneously with the aim of improving the performance of classifiers.
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A comparative study on online machine learning techniques for network traffic streams analysis
TL;DR: In this paper , the authors investigate and compare the OL techniques that facilitate data stream analytics in the networking domain and highlight the advantages of online learning in this regard, as well as the challenges associated with OL-based network traffic stream analysis, e.g., concept drift and the imbalanced classes.
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Empirical Comparisons for Combining Balancing and Feature Selection Strategies for Characterizing Football Players Using FIFA Video Game System
TL;DR: In this article, a large-scale comparison of characterizing football players into nine positions by using FIFA video game data is presented, whereas most of the previous studies in this field have focused on characterizing players into only three classes according to their positions.
Journal ArticleDOI
An empirical study of data intrinsic characteristics that make learning from imbalanced data difficult
Mario Dudjak,Goran Martinović +1 more
TL;DR: A contemporary empirical study of the behaviour and performance of five well-known classifiers on a large number of imbalanced datasets exhibiting numerous combinations of the stated characteristics to identify and rank difficulty factors when learning from imbalanced data, depending on the type of classification algorithm used.
References
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