850 research outputs found

    Similarity-based and Iterative Label Noise Filters for Monotonic Classification

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    Monotonic ordinal classification has received an increasing interest in the latest years. Building monotone models from these problems usually requires datasets that verify monotonic relationships among the samples. When the monotonic relationships are not met, changing the labels may be a viable option, but the risk is high: wrong label changes would completely change the information contained in the data. In this work, we tackle the construction of monotone datasets by removing the wrong or noisy examples that violate monotonicity restrictions. We propose two monotonic noise filtering algorithms to preprocess the ordinal datasets and improve the monotonic relations between instances. The experiments are carried out over eleven ordinal datasets, showing that the application of the proposed filters improve the prediction capabilities over different levels of noise

    Computability and analysis: the legacy of Alan Turing

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    We discuss the legacy of Alan Turing and his impact on computability and analysis.Comment: 49 page

    One-Class Classification: Taxonomy of Study and Review of Techniques

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    One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

    Acta Cybernetica : Volume 19. Number 1.

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    Ensemble diversity for class imbalance learning

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    This thesis studies the diversity issue of classification ensembles for class imbalance learning problems. Class imbalance learning refers to learning from imbalanced data sets, in which some classes of examples (minority) are highly under-represented comparing to other classes (majority). The very skewed class distribution degrades the learning ability of many traditional machine learning methods, especially in the recognition of examples from the minority classes, which are often deemed to be more important and interesting. Although quite a few ensemble learning approaches have been proposed to handle the problem, no in-depth research exists to explain why and when they can be helpful. Our objectives are to understand how ensemble diversity affects the classification performance for a class imbalance problem according to single-class and overall performance measures, and to make best use of diversity to improve the performance. As the first stage, we study the relationship between ensemble diversity and generalization performance for class imbalance problems. We investigate mathematical links between single-class performance and ensemble diversity. It is found that how the single-class measures change along with diversity falls into six different situations. These findings are then verified in class imbalance scenarios through empirical studies. The impact of diversity on overall performance is also investigated empirically. Strong correlations between diversity and the performance measures are found. Diversity shows a positive impact on the recognition of the minority class and benefits the overall performance of ensembles in class imbalance learning. Our results help to understand if and why ensemble diversity can help to deal with class imbalance problems. Encouraged by the positive role of diversity in class imbalance learning, we then focus on a specific ensemble learning technique, the negative correlation learning (NCL) algorithm, which considers diversity explicitly when creating ensembles and has achieved great empirical success. We propose a new learning algorithm based on the idea of NCL, named AdaBoost.NC, for classification problems. An ``ambiguity" term decomposed from the 0-1 error function is introduced into the training framework of AdaBoost. It demonstrates superiority in both effectiveness and efficiency. Its good generalization performance is explained by theoretical and empirical evidences. It can be viewed as the first NCL algorithm specializing in classification problems. Most existing ensemble methods for class imbalance problems suffer from the problems of overfitting and over-generalization. To improve this situation, we address the class imbalance issue by making use of ensemble diversity. We investigate the generalization ability of NCL algorithms, including AdaBoost.NC, to tackle two-class imbalance problems. We find that NCL methods integrated with random oversampling are effective in recognizing minority class examples without losing the overall performance, especially the AdaBoost.NC tree ensemble. This is achieved by providing smoother and less overfitting classification boundaries for the minority class. The results here show the usefulness of diversity and open up a novel way to deal with class imbalance problems. Since the two-class imbalance is not the only scenario in real-world applications, multi-class imbalance problems deserve equal attention. To understand what problems multi-class can cause and how it affects the classification performance, we study the multi-class difficulty by analyzing the multi-minority and multi-majority cases respectively. Both lead to a significant performance reduction. The multi-majority case appears to be more harmful. The results reveal possible issues that a class imbalance learning technique could have when dealing with multi-class tasks. Following this part of analysis and the promising results of AdaBoost.NC on two-class imbalance problems, we apply AdaBoost.NC to a set of multi-class imbalance domains with the aim of solving them effectively and directly. Our method shows good generalization in minority classes and balances the performance across different classes well without using any class decomposition schemes. Finally, we conclude this thesis with how the study has contributed to class imbalance learning and ensemble learning, and propose several possible directions for future research that may improve and extend this work

    Edge-centric inferential modeling & analytics

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    This work contributes to a real-time, edge-centric inferential modeling and analytics methodology introducing the fundamental mechanisms for (i) predictive models update and (ii) diverse models selection in distributed computing. Our objective in edge-centric analytics is the time-optimized model caching and selective forwarding at the network edge adopting optimal stopping theory, where communication overhead is significantly reduced as only inferred knowledge and sufficient statistics are delivered instead of raw data obtaining high quality of analytics. Novel model selection algorithms are introduced to fuse the inherent models' diversity over distributed edge nodes to support inferential analytics tasks to end-users/analysts, and applications in real-time. We provide statistical learning modeling and establish the corresponding mathematical analyses of our mechanisms along with comprehensive performance and comparative assessment using real data from different domains and showing its benefits in edge computing
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