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Naeem Seliya - One of the best experts on this subject based on the ideXlab platform.
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resource oriented selection of rule based Classification Models an empirical case study
Software Quality Journal, 2006Co-Authors: Taghi M. Khoshgoftaar, Angela Herzberg, Naeem SeliyaAbstract:The amount of resources allocated for software quality improvements is often not enough to achieve the desired software quality. Software quality Classification Models that yield a risk-based quality estimation of program modules, such as fault-prone (fp) and not fault-prone (nfp), are useful as software quality assurance techniques. Their usefulness is largely dependent on whether enough resources are available for inspecting the fp modules. Since a given development project has its own budget and time limitations, a resource-based software quality improvement seems more appropriate for achieving its quality goals. A Classification model should provide quality improvement guidance so as to maximize resource-utilization. We present a procedure for building software quality Classification Models from the limited resources perspective. The essence of the procedure is the use of our recently proposed Modified Expected Cost of MisClassification (MECM) measure for developing resource-oriented software quality Classification Models. The measure penalizes a model, in terms of costs of misClassifications, if the model predicts more number of fp modules than the number that can be inspected with the allotted resources. Our analysis is presented in the context of our Rule-Based Classification Modeling (RBCM) technique. An empirical case study of a large-scale software system demonstrates the promising results of using the MECM measure to select an appropriate resource-based rule-based Classification model.
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Resource-oriented software quality Classification Models
Journal of Systems and Software, 2005Co-Authors: Taghi M. Khoshgoftaar, Naeem Seliya, Angela HerzbergAbstract:Developing high-quality software within the allotted time and budget is a key element for a productive and successful software project. Software quality Classification Models that provide a risk-based quality estimation, such as fault-prone (fp) and not fault-prone (nfp), have proven their usefulness as software quality assurance techniques. However, their usefulness is largely dependent on the availability of resources for deploying quality improvements to modules predicted as fp. Since every project has its own special needs and specifications, we feel a Classification modeling approach based on resource availability is greatly warranted.We propose and demonstrate the use of a resource-based measure, i.e., "Modified Expected Cost of MisClassification" (MECM), for selecting and evaluating Classification Models. It is an extension of the "Expected Cost of MisClassification" (ECM) measure, which we have previously applied for model-evaluation purposes. The proposed measure facilitates building resource-oriented Classification Models and overcomes the limitation of ECM, which assumes that enough resources are available to enhance all modules predicted as fp. The primary aspect of MECM is that it penalizes a model, in terms of costs of misClassifications, if the model predicts more number of fp modules than the number that can be enhanced with the available resources. Based on the resources available for improving quality of software modules, a practitioner can use the proposed methodology to select a model that bestsuits the projects goals. Hence, the best possible and practical usage of the available resources can be achieved. The application, analysis, and benefits of MECM is shown by developing Models using Logistic Regression. It is concluded that the use of MECM is a promising approach for practical software quality improvement.
Angela Herzberg - One of the best experts on this subject based on the ideXlab platform.
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resource oriented selection of rule based Classification Models an empirical case study
Software Quality Journal, 2006Co-Authors: Taghi M. Khoshgoftaar, Angela Herzberg, Naeem SeliyaAbstract:The amount of resources allocated for software quality improvements is often not enough to achieve the desired software quality. Software quality Classification Models that yield a risk-based quality estimation of program modules, such as fault-prone (fp) and not fault-prone (nfp), are useful as software quality assurance techniques. Their usefulness is largely dependent on whether enough resources are available for inspecting the fp modules. Since a given development project has its own budget and time limitations, a resource-based software quality improvement seems more appropriate for achieving its quality goals. A Classification model should provide quality improvement guidance so as to maximize resource-utilization. We present a procedure for building software quality Classification Models from the limited resources perspective. The essence of the procedure is the use of our recently proposed Modified Expected Cost of MisClassification (MECM) measure for developing resource-oriented software quality Classification Models. The measure penalizes a model, in terms of costs of misClassifications, if the model predicts more number of fp modules than the number that can be inspected with the allotted resources. Our analysis is presented in the context of our Rule-Based Classification Modeling (RBCM) technique. An empirical case study of a large-scale software system demonstrates the promising results of using the MECM measure to select an appropriate resource-based rule-based Classification model.
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Resource-oriented software quality Classification Models
Journal of Systems and Software, 2005Co-Authors: Taghi M. Khoshgoftaar, Naeem Seliya, Angela HerzbergAbstract:Developing high-quality software within the allotted time and budget is a key element for a productive and successful software project. Software quality Classification Models that provide a risk-based quality estimation, such as fault-prone (fp) and not fault-prone (nfp), have proven their usefulness as software quality assurance techniques. However, their usefulness is largely dependent on the availability of resources for deploying quality improvements to modules predicted as fp. Since every project has its own special needs and specifications, we feel a Classification modeling approach based on resource availability is greatly warranted.We propose and demonstrate the use of a resource-based measure, i.e., "Modified Expected Cost of MisClassification" (MECM), for selecting and evaluating Classification Models. It is an extension of the "Expected Cost of MisClassification" (ECM) measure, which we have previously applied for model-evaluation purposes. The proposed measure facilitates building resource-oriented Classification Models and overcomes the limitation of ECM, which assumes that enough resources are available to enhance all modules predicted as fp. The primary aspect of MECM is that it penalizes a model, in terms of costs of misClassifications, if the model predicts more number of fp modules than the number that can be enhanced with the available resources. Based on the resources available for improving quality of software modules, a practitioner can use the proposed methodology to select a model that bestsuits the projects goals. Hence, the best possible and practical usage of the available resources can be achieved. The application, analysis, and benefits of MECM is shown by developing Models using Logistic Regression. It is concluded that the use of MECM is a promising approach for practical software quality improvement.
Taghi M. Khoshgoftaar - One of the best experts on this subject based on the ideXlab platform.
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resource oriented selection of rule based Classification Models an empirical case study
Software Quality Journal, 2006Co-Authors: Taghi M. Khoshgoftaar, Angela Herzberg, Naeem SeliyaAbstract:The amount of resources allocated for software quality improvements is often not enough to achieve the desired software quality. Software quality Classification Models that yield a risk-based quality estimation of program modules, such as fault-prone (fp) and not fault-prone (nfp), are useful as software quality assurance techniques. Their usefulness is largely dependent on whether enough resources are available for inspecting the fp modules. Since a given development project has its own budget and time limitations, a resource-based software quality improvement seems more appropriate for achieving its quality goals. A Classification model should provide quality improvement guidance so as to maximize resource-utilization. We present a procedure for building software quality Classification Models from the limited resources perspective. The essence of the procedure is the use of our recently proposed Modified Expected Cost of MisClassification (MECM) measure for developing resource-oriented software quality Classification Models. The measure penalizes a model, in terms of costs of misClassifications, if the model predicts more number of fp modules than the number that can be inspected with the allotted resources. Our analysis is presented in the context of our Rule-Based Classification Modeling (RBCM) technique. An empirical case study of a large-scale software system demonstrates the promising results of using the MECM measure to select an appropriate resource-based rule-based Classification model.
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Resource-oriented software quality Classification Models
Journal of Systems and Software, 2005Co-Authors: Taghi M. Khoshgoftaar, Naeem Seliya, Angela HerzbergAbstract:Developing high-quality software within the allotted time and budget is a key element for a productive and successful software project. Software quality Classification Models that provide a risk-based quality estimation, such as fault-prone (fp) and not fault-prone (nfp), have proven their usefulness as software quality assurance techniques. However, their usefulness is largely dependent on the availability of resources for deploying quality improvements to modules predicted as fp. Since every project has its own special needs and specifications, we feel a Classification modeling approach based on resource availability is greatly warranted.We propose and demonstrate the use of a resource-based measure, i.e., "Modified Expected Cost of MisClassification" (MECM), for selecting and evaluating Classification Models. It is an extension of the "Expected Cost of MisClassification" (ECM) measure, which we have previously applied for model-evaluation purposes. The proposed measure facilitates building resource-oriented Classification Models and overcomes the limitation of ECM, which assumes that enough resources are available to enhance all modules predicted as fp. The primary aspect of MECM is that it penalizes a model, in terms of costs of misClassifications, if the model predicts more number of fp modules than the number that can be enhanced with the available resources. Based on the resources available for improving quality of software modules, a practitioner can use the proposed methodology to select a model that bestsuits the projects goals. Hence, the best possible and practical usage of the available resources can be achieved. The application, analysis, and benefits of MECM is shown by developing Models using Logistic Regression. It is concluded that the use of MECM is a promising approach for practical software quality improvement.
Milos Hauskrecht - One of the best experts on this subject based on the ideXlab platform.
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SDM - Active Learning of Classification Models with Likert-Scale Feedback.
Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining, 2017Co-Authors: Yanbing Xue, Milos HauskrechtAbstract:Annotation of Classification data by humans can be a time-consuming and tedious process. Finding ways of reducing the annotation effort is critical for building the Classification Models in practice and for applying them to a variety of Classification tasks. In this paper, we develop a new active learning framework that combines two strategies to reduce the annotation effort. First, it relies on label uncertainty information obtained from the human in terms of the Likert-scale feedback. Second, it uses active learning to annotate examples with the greatest expected change. We propose a Bayesian approach to calculate the expectation and an incremental SVM solver to reduce the time complexity of the solvers. We show the combination of our active learning strategy and the Likert-scale feedback can learn Classification Models more rapidly and with a smaller number of labeled instances than methods that rely on either Likert-scale labels or active learning alone.
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active learning of Classification Models with likert scale feedback
SIAM International Conference on Data Mining, 2017Co-Authors: Yanbing Xue, Milos HauskrechtAbstract:Annotation of Classification data by humans can be a time-consuming and tedious process. Finding ways of reducing the annotation effort is critical for building the Classification Models in practice and for applying them to a variety of Classification tasks. In this paper, we develop a new active learning framework that combines two strategies to reduce the annotation effort. First, it relies on label uncertainty information obtained from the human in terms of the Likert-scale feedback. Second, it uses active learning to annotate examples with the greatest expected change. We propose a Bayesian approach to calculate the expectation and an incremental SVM solver to reduce the time complexity of the solvers. We show the combination of our active learning strategy and the Likert-scale feedback can learn Classification Models more rapidly and with a smaller number of labeled instances than methods that rely on either Likert-scale labels or active learning alone.
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Learning Classification Models with soft-label information.
Journal of the American Medical Informatics Association : JAMIA, 2013Co-Authors: Quang Nguyen, Hamed Valizadegan, Milos HauskrechtAbstract:Objective Learning of Classification Models in medicine often relies on data labeled by a human expert. Since labeling of clinical data may be time-consuming, finding ways of alleviating the labeling costs is critical for our ability to automatically learn such Models. In this paper we propose a new machine learning approach that is able to learn improved binary Classification Models more efficiently by refining the binary class information in the training phase with soft labels that reflect how strongly the human expert feels about the original class labels. Materials and methods Two types of methods that can learn improved binary Classification Models from soft labels are proposed. The first relies on probabilistic/numeric labels, the other on ordinal categorical labels. We study and demonstrate the benefits of these methods for learning an alerting model for heparin induced thrombocytopenia. The experiments are conducted on the data of 377 patient instances labeled by three different human experts. The methods are compared using the area under the receiver operating characteristic curve (AUC) score. Results Our AUC results show that the new approach is capable of learning Classification Models more efficiently compared to traditional learning methods. The improvement in AUC is most remarkable when the number of examples we learn from is small. Conclusions A new Classification learning framework that lets us learn from auxiliary soft-label information provided by a human expert is a promising new direction for learning Classification Models from expert labels, reducing the time and cost needed to label data.
Herzbergangela - One of the best experts on this subject based on the ideXlab platform.
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Resource-oriented software quality Classification Models
Journal of Systems and Software, 2005Co-Authors: M Khoshgoftaartaghi, Seliyanaeem, HerzbergangelaAbstract:Developing high-quality software within the allotted time and budget is a key element for a productive and successful software project. Software quality Classification Models that provide a risk-ba...