Balancing Exploration and Exploitation: A novel active learner for imbalanced data

Background: Backorders are an accepted abnormality affecting accumulation alternation and logistics, sales, chump service, and manufacturing, which generally leads to low sales and low chump satisfaction. A predictive archetypal can analyse which articles are best acceptable to acquaintance backorders giving the alignment advice and time to adjust, thereby demography accomplishes to aerate their profit. Objective: To address the issue of predicting backorders, this paper has proposed an un-supervised approach to backorder prediction using Deep Autoencoder. Method: In this paper, artificial intelligence paradigms are researched in order to introduce a predictive model for the present unbalanced data issues, where the number of products going on backorder is rare. Result: Un-supervised anomaly detection using deep auto encoders has shown better Area under the Receiver Operating Characteristic and precision-recall curves than supervised classification techniques employed with resampling techniques for imbalanced data problems. Conclusion: We demonstrated that Un-supervised anomaly detection methods specifically deep auto-encoders can be used to learn a good representation of the data. The method can be used as predictive model for inventory management and help to reduce bullwhip effect, raise customer satisfaction as well as improve operational management in the organization. This technology is expected to create the sentient supply chain of the future – able to feel, perceive and react to situations at an extraordinarily granular level

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Imbalanced Data Detection Kernel Method in Closed Systems

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.3652 ◽

2013 ◽

Vol 756-759 ◽

pp. 3652-3658

Author(s):

You Li Lu ◽

Jun Luo

Keyword(s):

Kernel Methods ◽

Kernel Method ◽

Imbalanced Data ◽

Data Detection ◽

Data Sets ◽

System Call ◽

Data Set ◽

Imbalanced Data Sets ◽

Lower Complexity ◽

Closed Systems

Under the study of Kernel Methods, this paper put forward two improved algorithm which called R-SVM & I-SVDD in order to cope with the imbalanced data sets in closed systems. R-SVM used K-means algorithm clustering space samples while I-SVDD improved the performance of original SVDD by imbalanced sample training. Experiment of two sets of system call data set shows that these two algorithms are more effectively and R-SVM has a lower complexity.

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How to measure uncertainty in uncertainty sampling for active learning

Machine Learning ◽

10.1007/s10994-021-06003-9 ◽

2021 ◽

Author(s):

Vu-Linh Nguyen ◽

Mohammad Hossein Shaker ◽

Eyke Hüllermeier

Keyword(s):

Machine Learning ◽

Active Learning ◽

Sampling Strategies ◽

Total Uncertainty ◽

Uncertainty Sampling ◽

Different Types ◽

Alternative Approaches ◽

Active Learner ◽

Probabilistic Nature ◽

Different Sources

AbstractVarious strategies for active learning have been proposed in the machine learning literature. In uncertainty sampling, which is among the most popular approaches, the active learner sequentially queries the label of those instances for which its current prediction is maximally uncertain. The predictions as well as the measures used to quantify the degree of uncertainty, such as entropy, are traditionally of a probabilistic nature. Yet, alternative approaches to capturing uncertainty in machine learning, alongside with corresponding uncertainty measures, have been proposed in recent years. In particular, some of these measures seek to distinguish different sources and to separate different types of uncertainty, such as the reducible (epistemic) and the irreducible (aleatoric) part of the total uncertainty in a prediction. The goal of this paper is to elaborate on the usefulness of such measures for uncertainty sampling, and to compare their performance in active learning. To this end, we instantiate uncertainty sampling with different measures, analyze the properties of the sampling strategies thus obtained, and compare them in an experimental study.

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Cluster-Wise Weighted NMF for Hyperspectral Images Unmixing with Imbalanced Data

Remote Sensing ◽

10.3390/rs13020268 ◽

2021 ◽

Vol 13 (2) ◽

pp. 268

Author(s):

Xiaochen Lv ◽

Wenhong Wang ◽

Hongfu Liu

Keyword(s):

Spatial Information ◽

Hyperspectral Image ◽

Imbalanced Data ◽

Reconstruction Error ◽

Hyperspectral Data ◽

Weight Matrix ◽

Hyperspectral Images ◽

Mixed Data ◽

Sparsity Constraints ◽

Additional Constraints

Hyperspectral unmixing is an important technique for analyzing remote sensing images which aims to obtain a collection of endmembers and their corresponding abundances. In recent years, non-negative matrix factorization (NMF) has received extensive attention due to its good adaptability for mixed data with different degrees. The majority of existing NMF-based unmixing methods are developed by incorporating additional constraints into the standard NMF based on the spectral and spatial information of hyperspectral images. However, they neglect to exploit the nature of imbalanced pixels included in the data, which may cause the pixels mixed with imbalanced endmembers to be ignored, and thus the imbalanced endmembers generally cannot be accurately estimated due to the statistical property of NMF. To exploit the information of imbalanced samples in hyperspectral data during the unmixing procedure, in this paper, a cluster-wise weighted NMF (CW-NMF) method for the unmixing of hyperspectral images with imbalanced data is proposed. Specifically, based on the result of clustering conducted on the hyperspectral image, we construct a weight matrix and introduce it into the model of standard NMF. The proposed weight matrix can provide an appropriate weight value to the reconstruction error between each original pixel and the reconstructed pixel in the unmixing procedure. In this way, the adverse effect of imbalanced samples on the statistical accuracy of NMF is expected to be reduced by assigning larger weight values to the pixels concerning imbalanced endmembers and giving smaller weight values to the pixels mixed by majority endmembers. Besides, we extend the proposed CW-NMF by introducing the sparsity constraints of abundance and graph-based regularization, respectively. The experimental results on both synthetic and real hyperspectral data have been reported, and the effectiveness of our proposed methods has been demonstrated by comparing them with several state-of-the-art methods.

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