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A Novel Active Learning Technique for Fetal Health Classification Based on XGBoost Classifier

IEEE ACCESS 2025年 13卷 9485-9497页

作者： Bhardwaj, Kaushal Goyal, Niyati Mittal, Bhavika Sharma, Vandna Shivhare, Shiv Naresh Bennett Univ Sch Comp Sci Engn & Technol Greater Noida 201310 Uttar Pradesh India

Ensuring safe pregnancy and reducing maternal and infant mortality rates require early prediction of fetal health. The application of machine learning algorithms in monitoring fetal health helps to improve the chances of timely intervention and better outcomes in the event of any possible health issues in fetuses. Existing studies offered to help this issue, typically by training models using a significant portion of the dataset, ranging mainly above 70%. The only existing active learning method in this field employs around 41% training samples to achieve 98% accuracy. This work presents a novel active learning technique to identify the most informative data samples to train a model, leading to high accuracy with a limited number of training samples. It employs a novel query function built upon uncertainty and diversity criteria which are derived based on properties of XGBoost classifier and distance from each other. For deriving uncertainty criterion the soft probabilities obtained for the unlabeled samples are used, while the distance among the uncertain samples in feature space is utilized for deriving diversity criterion. The proposed approach shows superior performance compared to all state-of-the-art methods. Through analysis and experimentation, the proposed solution achieves an accuracy greater than 99% using less than 20% of the dataset for training. This shows its efficacy and potential in the monitoring of fetal health. The code and dataset are available on the GitHub repository https://***/niyg7/fetal-health-dataset.

关键词： Training Accuracy Active learning Uncertainty Support vector machines Predictive models Machine learning Pregnancy Data models Classification algorithms Fetal health active learning XGBoost query function uncertainty diversity

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A cluster-assumption based batch mode active learning technique

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PATTERN RECOGNITION LETTERS 2012年第9期33卷 1042-1048页

作者： Patra, Swarnajyoti Bruzzone, Lorenzo Univ Trento Dept Informat Engn & Comp Sci I-38123 Trento Italy

In this paper, we propose an active learning technique for solving multiclass problems with support vector machine (SVM) classifiers. The technique is based on both uncertainty and diversity criteria. The uncertainty criterion is implemented by analyzing the one-dimensional output space of the SVM classifier. A simple histogram thresholding algorithm is used to find out the low density region in the SVM output space to identify the most uncertain samples. Then the diversity criterion exploits the kernel k-means clustering algorithm to select uncorrelated informative samples among the selected uncertain samples. To assess the effectiveness of the proposed method we compared it with other batch mode active learning techniques presented in the literature using one toy data set and three real data sets. Experimental results confirmed that the proposed technique provided a very good tradeoff among robustness to biased initial training samples, classification accuracy, computational complexity, and number of new labeled samples necessary to reach the convergence. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Active learning Cluster assumption Entropy query function Support vector machine

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A Fast Cluster-Assumption Based Active-Learning Technique for Classification of Remote Sensing Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2011年第5期49卷 1617-1626页

作者： Patra, Swarnajyoti Bruzzone, Lorenzo Univ Trento Dept Informat Engn & Comp Sci I-38123 Trento Italy

In this paper, we propose a simple, fast, and reliable active-learning technique for solving remote sensing image classification problems with support vector machine (SVM) classifiers. The main property of the proposed technique consists in its robustness to biased (poor) initial training sets. The presented method considers the 1-D output space of the classifier to identify the most uncertain samples whose labeling and inclusion in the training set involve a high probability to improve the classification results. A simple histogram-thresholding algorithm is used to find out the low-density (i.e., under the cluster assumption, the most uncertain) region in the 1-D SVM output space. To assess the effectiveness of the proposed method, we compared it with other active-learning techniques proposed in the remote sensing literature using multispectral and hyperspectral data. Experimental results confirmed that the proposed technique provided the best tradeoff among robustness to biased (poor) initial training samples, computational complexity, classification accuracy, and the number of new labeled samples necessary to reach convergence.

关键词： Active learning cluster assumption entropy hyperspectral imagery multispectral imagery query function remote sensing support vector machines (SVMs)

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A fast partition-based batch-mode active learning technique using SVM classifier

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SOFT COMPUTING 2018年第14期22卷 4627-4637页

作者： Singla, Anshu Patra, Swarnajyoti Thapar Univ Dept Comp Sci & Engn Patiala 147004 Punjab India Tezpur Univ Dept Comp Sci & Engn Tezpur 784028 India

The selection of informative samples known as query selection is the most challenging task in active learning. In this article, a batch-mode active learning technique is presented by defining a novel query function. The proposed technique first divides the unlabeled samples into uniform partitions in one-dimensional feature space according to their distribution in the original feature space. Then to select the most informative samples from the unlabeled pool, one sample from each partition is selected based on an uncertainty criterion defined by exploiting SVM classifier. The number of unlabeled samples selected at each iteration of active learning is determined automatically and depends on the number of non-empty partitions generated. The effectiveness of the proposed technique is measured by comparing it with four state-of-the-art techniques exist in the literature by using four different UCI repository data sets. The experimental analysis proved that the proposed technique is robust and computationally less demanding.

关键词： Active learning query function Support vector machine

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A Batch-Mode Active Learning Technique Based on Multiple Uncertainty for SVM Classifier

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2012年第3期9卷 497-501页

作者： Patra, Swarnajyoti Bruzzone, Lorenzo Univ Trento Dept Informat Engn & Comp Sci I-38123 Trento Italy

In this letter, we present a novel batch-mode active learning technique for solving multiclass classification problems by using the support vector machine classifier with the one-against-all architecture. The uncertainty of each unlabeled sample is measured by defining a criterion which not only considers the smallest distance to the decision hyperplanes but also takes into account the distances to other hyperplanes if the sample is within the margin of their decision boundaries. To select batch of most uncertain samples from all over the decision region, the uncertain regions of the classifiers are partitioned into multiple parts depending on the number of geometrical margins of binary classifiers passing on them. Then, a balanced number of most uncertain samples are selected from each part. To minimize the redundancy and keep the diversity among these samples, the kernel k-means clustering algorithm is applied to the set of uncertain samples, and the representative sample (medoid) from each cluster is selected for labeling. The effectiveness of the proposed method is evaluated by comparing it with other batch-mode active learning techniques existing in the literature. Experimental results on two different remote sensing data sets confirmed the effectiveness of the proposed technique.

关键词： Active learning hyperspectral imagery multispectral imagery query function remote sensing support vector machine (SVM)

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p3d-Python module for structural bioinformatics

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BMC BIOINFORMATICS 2009年第1期10卷 1-5页

作者： Fufezan, Christian Specht, Michael Univ Munster Inst Evolut & Biodivers D-48149 Munster Germany Univ Munster Inst Biochem & Biotechnol Pflanzen D-48143 Munster Germany

Background: High-throughput bioinformatic analysis tools are needed to mine the large amount of structural data via knowledge based approaches. The development of such tools requires a robust interface to access the structural data in an easy way. For this the Python scripting language is the optimal choice since its philosophy is to write an understandable source code. Results: p3d is an object oriented Python module that adds a simple yet powerful interface to the Python interpreter to process and analyse three dimensional protein structure files (PDB files). p3d's strength arises from the combination of a) very fast spatial access to the structural data due to the implementation of a binary space partitioning (BSP) tree, b) set theory and c) functions that allow to combine a and b and that use human readable language in the search queries rather than complex computer language. All these factors combined facilitate the rapid development of bioinformatic tools that can perform quick and complex analyses of protein structures. Conclusion: p3d is the perfect tool to quickly develop tools for structural bioinformatics using the Python scripting language.

关键词： Complex query Vector Operation Plane Class query function Structural Bioinformatics

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Uncertainty-based active ranking for document retrieval

Uncertainty-based active ranking for document retrieval

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7th International Conference on Machine Learning and Cybernetics

作者： Wang, Yang Kuai, Yu-Hao Huang, Ya-Lou Li, Dong Ni, Wei-Jian Nankai Univ Coll Software Tianjin 300071 Peoples R China Nankai Univ Coll Informat Technol Sci Tianjin 300071 Peoples R China

ISBN: (纸本)9781424420957

One of the main problems in information retrieval is ranking documents according to their relevance to users' queries. Learning to rank is considered as a promising approach for addressing the issue. However, like many, other supervised approaches, one of (he main problems with learning to rank is the lack of labeled data, as well as labeling instances to create a rank model is time-consuming and costly. Thus, if is beneficial to minimize the number of labeled instances. In this paper, we bring the idea of active learning into ranking problem, and propose a new active ranking approach for document retrieval, referred to as Active RSVM. Specifically,, we present an uncertainty- based query, function to estimate the uncertainty, of each instance, decide which instances can provide more information for the ranker and reduce the labeling cost. Experimental results on two real-world datasets show that our proposed active ranking algorithm can reduce the labeling cost greatly, without decreasing the ranking accuracy.

关键词： information retrieval learning to rank active learning ranking SVM query function

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A query-LEVEL ACTIVE SAMPLING APPROACH FOR LEARNING TO RANK

A QUERY-LEVEL ACTIVE SAMPLING APPROACH FOR LEARNING TO RANK

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International Conference on Machine Learning and Cybernetics

作者： Wang, Yang Huang, Ya-Lou Xie, Mao-Qiang Liu, Jie Lu, Min Liao, Zhen Nankai Univ Coll Informat Technol Sci Tianjin 300071 Peoples R China Nankai Univ Coll Software Tianjin 300071 Peoples R China

ISBN: (纸本)9781424447053

Learning to rank is becoming more and more popular In machine learning and information retrieval field. However, like many other supervised approaches, one of the main problems with learning to rank is lack of labeled data. Recently, there have been attempts to address the challenges In active sampling for learning to rank. But none of these methods take into consideration the differences between queries*. In this paper, we propose a novel active ranking framework on query-level which alms to employ different ranking models for different queries. Then, we used Rank SVM as a base ranker, realized a query-level active ranking algorithm and applied it to document retrieval. Experimental results on real-world dataset stow that our approach can reduce the labeling cost greatly without decreasing the ranking accuracy.

关键词： Information Retrieval Learning to Rank Active Learning query-level query function

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Active Learning for Hyperspectral Image Classification Using Kernel Sparse Representation Classifiers

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2023年 20卷 1页

作者： Bortiew, Amos Patra, Swarnajyoti Bruzzone, Lorenzo Tezpur Univ Dept Comp Sci & Engn Tezpur 784028 Assam India Univ Trento Dept Informat Engn & Comp Sci I-38123 Trento Italy

Active learning (AL) is one of the popular approaches that can mitigate some of the drawbacks of supervised classification. Although sparse representation classifier (SRC) has already proven to be a robust classifier and successfully used in many applications, it is seldom used jointly with AL. In this letter, we propose a novel AL technique for SRCs. In the proposed model, the query function is designed by combining uncertainty and diversity criteria, both of which are defined by using the SRC in kernel space. The proposed technique outperforms other state-of-the-art methods in terms of classification performance.

关键词： Uncertainty Kernel Dictionaries Diversity reception Redundancy Training Correlation Active learning (AL) hyperpsectral image kernel space query function sparse representation

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A query-LEVEL ACTIVE SAMPLING APPROACH FOR LEARNING TO RANK

A QUERY-LEVEL ACTIVE SAMPLING APPROACH FOR LEARNING TO RANK

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2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

作者： JIE LIU ZHEN LIAO MIN LU YANG WANG YA-LOU HUANG MAO-QIANG XIE College of Information Technology Science Nankai University Tianjin 300071 China College of Information Technology Science Nankai University Tianjin 300071 China College of Softw College of Software Nankai University Tianjin 300071 China

Learning to rank is becoming more and more popular in machine learning and information retrieval field. However, like many other supervised approaches, one of the main problems with learning to rank Is lack of labeled data. Recently, there have been attempts to address the challenges In active sampling for learning to rank. But none of these methods take into consideration the differences between queries. In this paper, we propose a novel active ranking framework on query-level which alms to employ different ranking models for different queries. Then, we used Rank SVM as a base ranker, realized a query-level active ranking algorithm and applied it to document retrieval. Experimental results on real-world data set show that our approach can reduce the labeling cost greatly without decreasing the ranking accuracy.

关键词： Information Retrieval Learning to Rank Active Learning query-level query function

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