The Experts below are selected from a list of 560169 Experts worldwide ranked by ideXlab platform
Chongsheng Zhang - One of the best experts on this subject based on the ideXlab platform.
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multi Imbalance an open source software for multi class Imbalance learning
Knowledge Based Systems, 2019Co-Authors: Chongsheng Zhang, Enislay Ramentol, Gaojuan Fan, Baojun Qiao, Hamido FujitaAbstract:Abstract Imbalance classification is one of the most challenging research problems in machine learning. Techniques for two-class Imbalance classification are relatively mature nowadays, yet multi-class Imbalance learning is still an open problem. Moreover, the community lacks a suitable software tool that can integrate the major works in the field. In this paper, we present Multi-Imbalance, an open source software package for multi-class Imbalanced data classification. It provides users with seven different categories of multi-class Imbalance learning algorithms, including the latest advances in the field. The source codes and documentations for Multi-Imbalance are publicly available at https://github.com/chongshengzhang/Multi_Imbalance .
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an empirical comparison on state of the art multi class Imbalance learning algorithms and a new diversified ensemble learning scheme
Knowledge Based Systems, 2018Co-Authors: Chongsheng ZhangAbstract:Abstract Class-Imbalance learning is one of the most challenging problems in machine learning. As a new and important direction in this field, multi-class Imbalanced data classification has attracted a great many research focus in recent years. In this paper, we first make a very comprehensive review on state-of-the-art classification algorithms for multi-class Imbalanced data. Moreover, we propose a new multi-class Imbalance classification algorithm, which is hereafter referred to as the Diversified Error Correcting Output Codes (DECOC) method. The main idea of DECOC is to combine the improved ECOC (Error Correcting Output Codes) method for tackling class Imbalance, and the diversified ensemble learning framework, which finds the best classification algorithm (out of many heterogeneous classification algorithms) for each individual sub-dataset resampled from the original data. We conduct experiments on 19 public datasets to empirically compare the performance of DECOC with 17 state-of-the-art multi-class Imbalance learning algorithms, using 4 different accuracy measures: overall accuracy, Geometric mean, F-measure, and Area Under Curve. Experimental results demonstrate that DECOC achieves significantly better accuracy performance than the other 17 algorithms on these accuracy metrics. To advance research in this field, we make all the source codes of DECOC and the above-mentioned 17 state-of-the-art algorithms for Imbalanced data classification be available at GitHub: https://github.com/chongshengzhang/Multi_Imbalance .
Hamido Fujita - One of the best experts on this subject based on the ideXlab platform.
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multi Imbalance an open source software for multi class Imbalance learning
Knowledge Based Systems, 2019Co-Authors: Chongsheng Zhang, Enislay Ramentol, Gaojuan Fan, Baojun Qiao, Hamido FujitaAbstract:Abstract Imbalance classification is one of the most challenging research problems in machine learning. Techniques for two-class Imbalance classification are relatively mature nowadays, yet multi-class Imbalance learning is still an open problem. Moreover, the community lacks a suitable software tool that can integrate the major works in the field. In this paper, we present Multi-Imbalance, an open source software package for multi-class Imbalanced data classification. It provides users with seven different categories of multi-class Imbalance learning algorithms, including the latest advances in the field. The source codes and documentations for Multi-Imbalance are publicly available at https://github.com/chongshengzhang/Multi_Imbalance .
Avanidhar Subrahmanyam - One of the best experts on this subject based on the ideXlab platform.
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order Imbalance and individual stock returns theory and evidence
Journal of Financial Economics, 2004Co-Authors: Tarun Chordia, Avanidhar SubrahmanyamAbstract:Abstract This paper studies the relation between order Imbalances and daily returns of individual stocks. Our tests are motivated by a model which considers how market makers dynamically accommodate autocorrelated Imbalances emanating from large traders who optimally choose to split their orders. Price pressures caused by autocorrelated Imbalances cause a positive relation between lagged Imbalances and returns, which reverses sign after controlling for the current Imbalance. We find empirical evidence consistent with these implications. We also find that Imbalance-based trading strategies yield statistically significant returns. Our results shed light on the role of inventory effects in daily stock price movements.
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Order Imbalance, Liquidity, and Market Returns
Journal of Financial Economics, 2002Co-Authors: Tarun Chordia, Richard Roll, Avanidhar SubrahmanyamAbstract:Traditionally, volume has provided the link between trading activity and returns. We focus on a hitherto unexplored but intuitive measure of trading activity: the aggregate daily order Imbalance on the New York Stock Exchange. Signed order Imbalances increase (decrease) following market declines (rises), which reveals that investors are contrarians on aggregate. Order Imbalances in either direction, either excess buy or sell orders, reduce liquidity. Market-wide returns are strongly affected by contemporaneous and lagged order Imbalances. Market-wide returns reverse themselves after high negative Imbalance, large negative return days; the magnitude of this reversal is partially predictable from the level of the Imbalance and return. Even after controlling for aggregate market volume and liquidity, market returns are affected by order Imbalance.
Jinghao Xue - One of the best experts on this subject based on the ideXlab platform.
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Adjusting the Imbalance ratio by the dimensionality of Imbalanced data
Pattern Recognition Letters, 2020Co-Authors: Rui Zhu, Yiwen Guo, Jinghao XueAbstract:Class-Imbalance extent metrics measure how Imbalanced the data are. In pattern classification, it is usually expected that the higher the Imbalance extent, the worse the classification performance, and thus an appropriate Imbalance extent metric should show a negative correlation with the classification performance. Existing metrics, such as the popular Imbalance ratio (IR), only consider the effect of the sample sizes of different classes. However, we note that the dimensionality of Imbalanced data also affects the classification performance. Datasets with the same IR can present distinct classification performances when their dimensionalities are different, making IR suboptimal to reflect the Imbalance extent for classification. We also observe that the classification performance becomes better with more discriminative features. Inspired by these observations, we propose a new Imbalance extent metric, the adjusted IR, by adding a penalty term of the number of discriminative features that is effectively determined by the Pearson correlation test. The adjusted IR adaptively revises the IR when the number of discriminative features varies. The empirical studies demonstrate the effectiveness of the adjusted IR, in terms of its better negative correlation with the classification performance.
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lrid a new metric of multi class Imbalance degree based on likelihood ratio test
Pattern Recognition Letters, 2018Co-Authors: Rui Zhu, Ziyu Wang, Guijin Wang, Jinghao XueAbstract:In this paper, we introduce a new likelihood ratio Imbalance degree (LRID) to measure the class-Imbalance extent of multi-class data. Imbalance ratio (IR) is usually used to measure class-Imbalance extent in Imbalanced learning problems. However, IR cannot capture the detailed information in the class distribution of multi-class data, because it only utilises the information of the largest majority class and the smallest minority class. Imbalance degree (ID) has been proposed to solve the problem of IR for multi-class data. However, we note that improper use of distance metric in ID can have harmful effect on the results. In addition, ID assumes that data with more minority classes are more Imbalanced than data with less minority classes, which is not always true in practice. Thus ID cannot provide reliable measurement when the assumption is violated. In this paper, we propose a new metric based on the likelihood-ratio test, LRID, to provide a more reliable measurement of class-Imbalance extent for multi-class data. Experiments on both simulated and real data show that LRID is competitive with IR and ID, and can reduce the negative correlation with F1 scores by up to 0.55.
Rui Zhu - One of the best experts on this subject based on the ideXlab platform.
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Adjusting the Imbalance ratio by the dimensionality of Imbalanced data
Pattern Recognition Letters, 2020Co-Authors: Rui Zhu, Yiwen Guo, Jinghao XueAbstract:Class-Imbalance extent metrics measure how Imbalanced the data are. In pattern classification, it is usually expected that the higher the Imbalance extent, the worse the classification performance, and thus an appropriate Imbalance extent metric should show a negative correlation with the classification performance. Existing metrics, such as the popular Imbalance ratio (IR), only consider the effect of the sample sizes of different classes. However, we note that the dimensionality of Imbalanced data also affects the classification performance. Datasets with the same IR can present distinct classification performances when their dimensionalities are different, making IR suboptimal to reflect the Imbalance extent for classification. We also observe that the classification performance becomes better with more discriminative features. Inspired by these observations, we propose a new Imbalance extent metric, the adjusted IR, by adding a penalty term of the number of discriminative features that is effectively determined by the Pearson correlation test. The adjusted IR adaptively revises the IR when the number of discriminative features varies. The empirical studies demonstrate the effectiveness of the adjusted IR, in terms of its better negative correlation with the classification performance.
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lrid a new metric of multi class Imbalance degree based on likelihood ratio test
Pattern Recognition Letters, 2018Co-Authors: Rui Zhu, Ziyu Wang, Guijin Wang, Jinghao XueAbstract:In this paper, we introduce a new likelihood ratio Imbalance degree (LRID) to measure the class-Imbalance extent of multi-class data. Imbalance ratio (IR) is usually used to measure class-Imbalance extent in Imbalanced learning problems. However, IR cannot capture the detailed information in the class distribution of multi-class data, because it only utilises the information of the largest majority class and the smallest minority class. Imbalance degree (ID) has been proposed to solve the problem of IR for multi-class data. However, we note that improper use of distance metric in ID can have harmful effect on the results. In addition, ID assumes that data with more minority classes are more Imbalanced than data with less minority classes, which is not always true in practice. Thus ID cannot provide reliable measurement when the assumption is violated. In this paper, we propose a new metric based on the likelihood-ratio test, LRID, to provide a more reliable measurement of class-Imbalance extent for multi-class data. Experiments on both simulated and real data show that LRID is competitive with IR and ID, and can reduce the negative correlation with F1 scores by up to 0.55.