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Relevant bibliographies by topics / Test-Cost-Sensitive Learning

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Academic literature on the topic 'Test-Cost-Sensitive Learning'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Test-Cost-Sensitive Learning"

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Mahdi, Naghibi, Anvari Reza, Forghani Ali, and Minaei Behrouz. "Test-cost-sensitive Convolutional Neural Networks with Expert Branches." Signal & Image Processing: An International Journal (SIPIJ) 10, no. 5 (2019): 15–27. https://doi.org/10.5281/zenodo.3541564.

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It has been proven that deeper convolutional neural networks (CNN) can result in better accuracy in many problems, but this accuracy comes with a high computational cost. Also, input instances have not the same difficulty. As a solution for accuracy vs. computational cost dilemma, we introduce a new test-cost-sensitive method for convolutional neural networks. This method trains a CNN with a set of auxiliary outputs and expert branches in some middle layers of the network. The expert branches decide to use a shallower part of the network or going deeper to the end, based on the difficulty of i

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Mirhashemi, Mohammad, Reza Anvari, Morteza Barari, and Nasser Mozayani. "Test-Cost Sensitive Ensemble of Classifiers Using Reinforcement Learning." Revue d'Intelligence Artificielle 34, no. 2 (2020): 143–50. http://dx.doi.org/10.18280/ria.340204.

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Qiu, Chen, Liangxiao Jiang, and Chaoqun Li. "Randomly selected decision tree for test-cost sensitive learning." Applied Soft Computing 53 (April 2017): 27–33. http://dx.doi.org/10.1016/j.asoc.2016.12.047.

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Zhao, Hong, Fan Min, and William Zhu. "Test-Cost-Sensitive Attribute Reduction of Data with Normal Distribution Measurement Errors." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/946070.

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The measurement error with normal distribution is universal in applications. Generally, smaller measurement error requires better instrument and higher test cost. In decision making, we will select an attribute subset with appropriate measurement error to minimize the total test cost. Recently, error-range-based covering rough set with uniform distribution error was proposed to investigate this issue. However, the measurement errors satisfy normal distribution instead of uniform distribution which is rather simple for most applications. In this paper, we introduce normal distribution measureme

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Wang, Tao, Zhenxing Qin, Zhi Jin, and Shichao Zhang. "Handling over-fitting in test cost-sensitive decision tree learning by feature selection, smoothing and pruning." Journal of Systems and Software 83, no. 7 (2010): 1137–47. http://dx.doi.org/10.1016/j.jss.2010.01.002.

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Febriantono, M. Aldiki, Sholeh Hadi Pramono, and Rahmadwati Rahmadwati. "Perbandingan Metode Cost Sensitive pada Decision Tree dan NaÃ¯ve Bayes untuk Klasifikasi Data Multiclass." Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) 14, no. 1 (2020): 21–26. http://dx.doi.org/10.21776/jeeccis.v14i1.625.

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Abstrakâ€“ Knowledge discovery is the method of extracting information from data in making informed decisions. Seeing as classifiers do have a lot of learning patterns in the data, testing an imbalanced dataset becomes a major classification issue. The cost-sensitive approach on the decision tree C4.5 and nave Bayes is used to solve the rule of misclassification. The glass, lympografi, vehicle, thyroid, and wine datasets were collected from the UCI Repository and included in this analysis. Preprocessing attribute selection with particle swarm optimization was used to process the data collectio

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Ma, Jun, Jiande Wu, and Xiaodong Wang. "Fault Diagnosis Method of Check Valve Based on Multikernel Cost-Sensitive Extreme Learning Machine." Complexity 2017 (2017): 1–19. http://dx.doi.org/10.1155/2017/8395252.

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Check valve is one of the most important components and most easily damaged parts in high pressure diaphragm pump, which is a typical representative of reciprocating machinery. In order to ensure the normal operation of the pump, it is necessary to monitor its running state and diagnose fault. However, in the fault diagnosis of check valve, the classification models with single kernel function can not fully interpret the classification decision function, and meanwhile unreasonable assumption of diagnostic cost equalization has a significant impact on classification results. Therefore, the mult

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Li, Dongdong, Yingchun Yang, and Weihui Dai. "Cost-Sensitive Learning for Emotion Robust Speaker Recognition." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/628516.

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In the field of information security, voice is one of the most important parts in biometrics. Especially, with the development of voice communication through the Internet or telephone system, huge voice data resources are accessed. In speaker recognition, voiceprint can be applied as the unique password for the user to prove his/her identity. However, speech with various emotions can cause an unacceptably high error rate and aggravate the performance of speaker recognition system. This paper deals with this problem by introducing a cost-sensitive learning technology to reweight the probability

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Santiso, Sara, Arantza Casillas, and Alicia Pérez. "The class imbalance problem detecting adverse drug reactions in electronic health records." Health Informatics Journal 25, no. 4 (2018): 1768–78. http://dx.doi.org/10.1177/1460458218799470.

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This work focuses on adverse drug reaction extraction tackling the class imbalance problem. Adverse drug reactions are infrequent events in electronic health records, nevertheless, it is compulsory to get them documented. Text mining techniques can help to retrieve this kind of valuable information from text. The class imbalance was tackled using different sampling methods, cost-sensitive learning, ensemble learning and one-class classification and the Random Forest classifier was used. The adverse drug reaction extraction model was inferred from a dataset that comprises real electronic health

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Ji, Min, Lanfa Liu, and Manfred Buchroithner. "Identifying Collapsed Buildings Using Post-Earthquake Satellite Imagery and Convolutional Neural Networks: A Case Study of the 2010 Haiti Earthquake." Remote Sensing 10, no. 11 (2018): 1689. http://dx.doi.org/10.3390/rs10111689.

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Earthquake is one of the most devastating natural disasters that threaten human life. It is vital to retrieve the building damage status for planning rescue and reconstruction after an earthquake. In cases when the number of completely collapsed buildings is far less than intact or less-affected buildings (e.g., the 2010 Haiti earthquake), it is difficult for the classifier to learn the minority class samples, due to the imbalance learning problem. In this study, the convolutional neural network (CNN) was utilized to identify collapsed buildings from post-event satellite imagery with the propo

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Book chapters on the topic "Test-Cost-Sensitive Learning"

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Sheng, Shengli, Charles X. Ling, and Qiang Yang. "Simple Test Strategies for Cost-Sensitive Decision Trees." In Machine Learning: ECML 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11564096_36.

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Tait, David S., Ellen E. Bowman, Silke Miller, Mary Dovlatyan, Connie Sanchez, and Verity J. Brown. "Escitalopram Restores Reversal Learning Impairments in Rats with Lesions of Orbital Frontal Cortex." In Language, Cognition, and Mind. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-50200-3_18.

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AbstractThe term ‘cognitive structures’ is used to describe the fact that mental models underlie thinking, reasoning and representing. Cognitive structures generally improve the efficiency of information processing by providing a situational framework within which there are parameters governing the nature and timing of information and appropriate responses can be anticipated. Unanticipated events that violate the parameters of the cognitive structure require the cognitive model to be updated, but this comes at an efficiency cost. In reversal learning a response that had been reinforced is no l

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Freitas, Alberto, Pavel Brazdil, and Altamiro Costa-Pereira. "Cost-Sensitive Learning in Medicine." In Machine Learning. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-818-7.ch607.

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This chapter introduces cost-sensitive learning and its importance in medicine. Health managers and clinicians often need models that try to minimize several types of costs associated with healthcare, including attribute costs (e.g. the cost of a specific diagnostic test) and misclassification costs (e.g. the cost of a false negative test). In fact, as in other professional areas, both diagnostic tests and its associated misclassification errors can have significant financial or human costs, including the use of unnecessary resource and patient safety issues. This chapter presents some concept

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Freitas, Alberto, and Altamiro Costa-Pereira. "Learning Cost-Sensitive Decision Trees to Support Medical Diagnosis." In Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-748-5.ch013.

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Classification plays an important role in medicine, especially for medical diagnosis. Real-world medical applications often require classifiers that minimize the total cost, including costs for wrong diagnosis (misclassifications costs) and diagnostic test costs (attribute costs). There are indeed many reasons for considering costs in medicine, as diagnostic tests are not free and health budgets are limited. In this chapter, the authors have defined strategies for cost-sensitive learning. They have developed an algorithm for decision tree induction that considers various types of costs, includ

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Zhang, Ting, Jiang Lu, Rui Ma, Koushik K. M., and Xin Li. "Low-Cost, Home-Oriented Neuro-Patient Monitoring." In Advances in Medical Technologies and Clinical Practice. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9740-9.ch014.

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Despite the extremely high medical cost of neuro-disorder diseases (NDDs), up to this point we still rely on labor-intensive observations to determine neuro-disorder symptoms. Therefore, it is critical to design a gait anomaly and motor disorder (GAMD) recognition system for accurate capture of NDD symptoms. Such an automatic GAMD monitoring system has to be low-cost, and uses highly motion-sensitive sensors and accurate GAMD pattern recognition algorithms. In this chapter we have introduced our low-cost, home-oriented system architecture that aims to monitor neurodisorder patients. Our system

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Conference papers on the topic "Test-Cost-Sensitive Learning"

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Dumpala, Sri Harsha, Rupayan Chakraborty, and Sunil Kumar Kopparapu. "A Novel Data Representation for Effective Learning in Class Imbalanced Scenarios." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/290.

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Class imbalance refers to the scenario where certain classes are highly under-represented compared to other classes in terms of the availability of training data. This situation hinders the applicability of conventional machine learning algorithms to most of the classification problems where class imbalance is prominent. Most existing methods addressing class imbalance either rely on sampling techniques or cost-sensitive learning methods; thus inheriting their shortcomings. In this paper, we introduce a novel approach that is different from sampling or cost-sensitive learning based techniques,

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Hou, Shaokang, Li Cheng, and Yaoru Liu. "Advance Prediction of Rockmass Conditions During TBM Tunnelling Based on Cost-Sensitive Learning Under Imbalance Dataset." In 57th U.S. Rock Mechanics/Geomechanics Symposium. ARMA, 2023. http://dx.doi.org/10.56952/arma-2023-0651.

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ABSTRACT The uncertainty of rockmass conditions will be an important factor causing TBM construction risks and inefficient tunnelling. Currently, prediction of rockmass classification based on TBM operation data and machine learning models has been proved feasible by many researchers. However, due to the sample imbalance problem in the previous studies, the prediction accuracy of minority rockmass class is relatively poor. To overcome the above problem, this study proposed a rockmass classification advance prediction method based on cost-sensitive learning (CSL), which using 10 key TBM operati

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Song, Hyunseung, Dong Hyuk Lee, and Hyun Chung. "Development of Prediction Model for Vehicle Road Load Using Machine Learning." In WCX SAE World Congress Experience. SAE International, 2025. https://doi.org/10.4271/2025-01-8258.

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<div class="section abstract"><div class="htmlview paragraph">In the modern automotive industry, improving fuel efficiency while reducing carbon emissions is a critical challenge. To address this challenge, accurately measuring a vehicle’s road load is essential. The current methodology, widely adopted by national guidelines, follows the coastdown test procedure. However, coastdown tests are highly sensitive to environmental conditions, which can lead to inconsistencies across test runs. Previous studies have mainly focused on the impact of independent variables on coastdown result

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Ma, Pingchuan, Shuai Wang, and Jin Liu. "Metamorphic Testing and Certified Mitigation of Fairness Violations in NLP Models." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/64.

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Natural language processing (NLP) models have been increasingly used in sensitive application domains including credit scoring, insurance, and loan assessment. Hence, it is critical to know that the decisions made by NLP models are free of unfair bias toward certain subpopulation groups. In this paper, we propose a novel framework employing metamorphic testing, a well-established software testing scheme, to test NLP models and find discriminatory inputs that provoke fairness violations. Furthermore, inspired by recent breakthroughs in the certified robustness of machine learning, we formulate

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Yin, Chenfei, and Yu Yang. "The Prediction of Fatigue Life Basing Random Forest Algorithm." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-72591.

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Abstract The fatigue performance of test pieces is sensitive to various influence factors. If one factor changes, the fatigue life will differ greatly. For the changes of each factors, the fatigue test must be carried on, which will increase the test cost. In this paper, in order to solve this problem, basing the machine learning method, we establish the random forest regression model to conduct a material fatigue fracture life prediction research for the 7050-T7451 aluminum alloy. For the 7050-T7451 aluminum alloy standard smooth test pieces considering six detailed factors, the fatigue test

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Zhang, Xinyan, Kai Shant, Zhipeng Tan, and Dan Feng. "CSLE: A Cost-sensitive Learning Engine for Disk Failure Prediction in Large Data Centers." In 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2022. http://dx.doi.org/10.23919/date54114.2022.9774751.

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Hussain, Sadam. "Data-Driven Facies Prediction Using Surface Drilling Parameters and Formation Elastic Properties – A Machine Learning Approach." In PAPG/SPE Pakistan Section Annual Technical Symposium and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/217363-ms.

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Abstract Unconventional reservoirs comprise of various heterogeneous productive and non-productive units which can be correlated with facies. To focus a target zone during drilling, it is essential to understand and identify unique zones in real-time. However, real-time LWD/MWD tools provide formation properties data with depth and time delay. Machine learning (ML) can help in predicting productive/non-productive facies/rock types without any time and depth delay enabling early decisions resulting in optimization of rig time and cost. In this study, ML approach has been employed to predict the

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Afraj, Shahabaz, Dennis Böhmländer, Ondrej Vaculin, and Luděk Hynčík. "Quantification methodology for crash behavior comparison between virtual crash simulations and real-time crash tests." In FISITA World Congress 2021. FISITA, 2021. http://dx.doi.org/10.46720/f2021-pif-072.

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"The automotive OEMs undertake a large amount of research and engineering work for the development of passive safety systems of their new vehicle models. The development of automotive passive safety system is susceptible to errors due to even minor variations in structural properties, material properties, and/or manufacturing process parameters. Assuming the sensor mounting positions and other external dependencies are fixed, there are data analysis models that can cause variations in the triggering of restraint systems. Every new development requires a fine calibration of the algorithm to tri

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Yin, Shenghao, Jun Zhou, Keisuke Osawa, Kei Nakagawa, and Eiichiro Tanaka. "Wearable sit-to-stand-up (STS) Guiding Device Using Asymmetric Vibration Speaker." In 16th International Conference on Applied Human Factors and Ergonomics (AHFE 2025). AHFE International, 2025. https://doi.org/10.54941/ahfe1006198.

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The lack of physical therapist (PT) resources has become a global problem [1]. The sit-to-stand-up (STS) training is one of the most important PT programs to improve the elderly’s mobility and prevent falling. In this research, a wearable (STS) motion navigation system utilizing asymmetric vibration techniques has been developed. Aiming to help the elderly do PT programs at their own house instead of paying the high cost and difficulty of making an appointment for PT.The asymmetric vibration method in this research uses a vibration speaker to play the asymmetric amplitude signal that drives th

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Reports on the topic "Test-Cost-Sensitive Learning"

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Bray, Jonathan, Ross Boulanger, Misko Cubrinovski, et al. U.S.—New Zealand— Japan International Workshop, Liquefaction-Induced Ground Movement Effects, University of California, Berkeley, California, 2-4 November 2016. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, 2017. http://dx.doi.org/10.55461/gzzx9906.

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There is much to learn from the recent New Zealand and Japan earthquakes. These earthquakes produced differing levels of liquefaction-induced ground movements that damaged buildings, bridges, and buried utilities. Along with the often spectacular observations of infrastructure damage, there were many cases where well-built facilities located in areas of liquefaction-induced ground failure were not damaged. Researchers are working on characterizing and learning from these observations of both poor and good performance. The “Liquefaction-Induced Ground Movements Effects” workshop provided an opp

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