On Multi‐class Cost‐sensitive Learning

Zhou, Zhi‐Hua; Liu, Xuying

doi:10.1111/j.1467-8640.2010.00358.x

Cited by 269 publications

(157 citation statements)

References 20 publications

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“…This requires a good insight into the modified learning algorithm and a precise identification of reasons for its failure in mining skewed distributions. The most popular branch is cost-sensitive approaches [67]. Here, given learner is modified to incorporate varying penalty for each of considered groups of examples.…”

Section: Tackling Imbalanced Datamentioning

confidence: 99%

Learning from imbalanced data: open challenges and future directions

2016

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Despite more than two decades of continuous development learning from imbalanced data is still a focus of intense research. Starting as a problem of skewed distributions of binary tasks, this topic evolved way beyond this conception. With the expansion of machine learning and data mining, combined with the arrival of big data era, we have gained a deeper insight into the nature of imbalanced learning, while at the same time facing new emerging challenges. Data-level and algorithm-level methods are constantly being improved and hybrid approaches gain increasing popularity. Recent trends focus on analyzing not only the disproportion between classes, but also other difficulties embedded in the nature of data. New real-life problems motivate researchers to focus on computationally efficient, adaptive and real-time methods. This paper aims at discussing open issues and challenges that need to be addressed to further develop the field of imbalanced learning. Seven vital areas of research in this topic are identified, covering the full spectrum of learning from imbalanced data: classification, regression, clustering, data streams, big data analytics and applications, e.g., in social media and computer vision. This paper provides a discussion and suggestions concerning lines of future research for each of them.

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Section: Tackling Imbalanced Datamentioning

confidence: 99%

Learning from imbalanced data: open challenges and future directions

2016

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“…2) Design of specific algorithms (solutions at the algorithmic level) [5], [10], [31] : In this case, a traditional classifier is adapted to deal directly with the imbalance between the classes, for example, modifying the cost per class [26] or adjusting the probability estimation in the leaves of a decision tree to favor the positive class [46]. 3) Cost-sensitive solutions [14], [42], [50], [51] : These kind of methods incorporate solutions at data level, at algorithmic level, or at both levels together, that try to minimize higher cost errors. Let C (+, −) denote the cost of misclassifying a positive (minority class) instance as a negative (majority class) instance and C (−, +) the cost of the inverse case.…”

Section: A Two-class Imbalanced Classification: Models and Evaluationmentioning

confidence: 99%

IFROWANN: Imbalanced Fuzzy-Rough Ordered Weighted Average Nearest Neighbor Classification

Ramentol

Vluymans

Verbiest

et al. 2015

IEEE Trans. Fuzzy Syst.

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Abstract-Imbalanced classification deals with learning from data with a disproportional number of samples in its classes. Traditional classifiers exhibit poor behavior when facing this kind of data because they do not take into account the imbalanced class distribution. Four main kinds of solutions exist to solve this problem: modifying the data distribution, modifying the learning algorithm for considering the imbalance representation, including the use of costs for data samples, and ensemble methods. In this paper, we adopt the second type of solution and introduce a classification algorithm for imbalanced data that uses fuzzy rough set theory and ordered weighted average aggregation. The proposal considers different strategies to build a weight vector to take into account data imbalance. Our methods are validated by an extensive experimental study, showing statistically better results than 13 other state-of-the-art methods.Index Terms-Fuzzy rough sets, imbalanced classification, machine learning, ordered weighted average (OWA).

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“…The class-imbalance problem can thus be considered a cost-sensitive problem where the costs are unequal and unknown [12]. Most cost-sensitive learning methods are actually based on rescaling [20], and therefore it is natural that by assigning the appropriate misclassification cost for each class, cost-sensitive approaches can be used to deal with the class-imbalance problems.…”

Section: Related Workmentioning

confidence: 99%

“…Rescaling [8] is a general method for cost-sensitive and class-imbalance problems which changes the distribution of the original data [20]. As there are many methods that can change the distribution, rescaling can be realized in numerous ways (e.g., by sampling, instance-weighting, threshold moving, etc.).…”

Section: Related Workmentioning

confidence: 99%

Building Decision Trees for the Multi-class Imbalance Problem

Hoens

Chawla

et al. 2012

Lecture Notes in Computer Science

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Abstract. Learning in imbalanced datasets is a pervasive problem prevalent in a wide variety of real-world applications. In imbalanced datasets, the class of interest is generally a small fraction of the total instances, but misclassification of such instances is often expensive. While there is a significant body of research on the class imbalance problem for binary class datasets, multi-class datasets have received considerably less attention. This is partially due to the fact that the multi-class imbalance problem is often much harder than its related binary class problem, as the relative frequency and cost of each of the classes can vary widely from dataset to dataset. In this paper we study the multi-class imbalance problem as it relates to decision trees (specifically C4.4 and HDDT), and develop a new multi-class splitting criterion. From our experiments we show that multi-class Hellinger distance decision trees, when combined with decomposition techniques, outperform C4.4.

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On Multi‐class Cost‐sensitive Learning

Cited by 269 publications

References 20 publications

Learning from imbalanced data: open challenges and future directions

Learning from imbalanced data: open challenges and future directions

IFROWANN: Imbalanced Fuzzy-Rough Ordered Weighted Average Nearest Neighbor Classification

Building Decision Trees for the Multi-class Imbalance Problem

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