scholarly journals Identifying Mislabeled Training Data

1999 ◽  
Vol 11 ◽  
pp. 131-167 ◽  
Author(s):  
C. E. Brodley ◽  
M. A. Friedl
Keyword(s):  
Classification Accuracy ◽  
Majority Vote ◽  
Learning Algorithms ◽  
Empirical Evaluation ◽  
Training Data ◽  
Noise Levels ◽  
New Approach ◽  
Good Data ◽  
Bad Data

This paper presents a new approach to identifying and eliminating mislabeled training instances for supervised learning. The goal of this approach is to improve classification accuracies produced by learning algorithms by improving the quality of the training data. Our approach uses a set of learning algorithms to create classifiers that serve as noise filters for the training data. We evaluate single algorithm, majority vote and consensus filters on five datasets that are prone to labeling errors. Our experiments illustrate that filtering significantly improves classification accuracy for noise levels up to 30 percent. An analytical and empirical evaluation of the precision of our approach shows that consensus filters are conservative at throwing away good data at the expense of retaining bad data and that majority filters are better at detecting bad data at the expense of throwing away good data. This suggests that for situations in which there is a paucity of data, consensus filters are preferable, whereas majority vote filters are preferable for situations with an abundance of data.

scholarly journals A New Approach to Assess the Quality of Small High-Speed Centrifugal Fans Using Noise Measurement

1999 ◽  
Vol 5 (2) ◽  
pp. 147-153 ◽  
Author(s):  
Dingjun Cui ◽  
Ian A. Craighead
Keyword(s):  
Signal Processing ◽  
Quality Assessment ◽  
Condition Monitoring ◽  
High Speed ◽  
Noise Levels ◽  
New Approach ◽  
Commercial Market ◽  
Centrifugal Fans ◽  
Special Approach

The requirements for a special approach for the quality assessment of small high-speed centrifugal fans are outlined and a new parameter designating the noise levels from the product in comprehensive form will be discussed and described as a criterion for such quality assessment.By applying techniques of signal processing and condition monitoring, the sources of the vibration and noise in different sections of the product can be identified, then the noise from each source from different components can be determined. Using this criterion, more aspects of the quality of the products can be assessed and suggestions to improve the quality of the products can be made. Finally, the assessment of a number ofvacuum cleaner motor/fan units available in the commercial market will be presented and compared with conventional specifications. It will be shown that the new parameter provides a more useful indication of appliance quality.

scholarly journals ROC curve, lift chart and calibration plot

2006 ◽  
Vol 3 (1) ◽  
Author(s):  
Miha Vuk ◽  
Tomaž Curk
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Roc Curve ◽  
Classification Accuracy ◽  
Empirical Evaluation ◽  
Calibration Plot ◽  
Mathematical Framework ◽  
Classification Models ◽  
Classification Quality

This paper presents ROC curve, lift chart and calibration plot, three well known graphical techniques that are useful for evaluating the quality of classification models used in data mining and machine learning. Each technique, normally used and studied separately, defines its own measure of classification quality and its visualization. Here, we give a brief survey of the methods and establish a common mathematical framework which adds some new aspects, explanations and interrelations between these techniques. We conclude with an empirical evaluation and a few examples on how to use the presented techniques to boost classification accuracy.

scholarly journals Learning Generalized Unsolvability Heuristics for Classical Planning

2021 ◽  
Author(s):  
Simon Ståhlberg ◽  
Guillem Francès ◽  
Jendrik Seipp
Keyword(s):  
Recent Work ◽  
Classification Accuracy ◽  
Supervised Classification ◽  
Learning Algorithms ◽  
Logical Form ◽  
Training Data ◽  
Goal State ◽  
Empirical Results ◽  
First Order ◽  
High Classification Accuracy

Recent work in classical planning has introduced dedicated techniques for detecting unsolvable states, i.e., states from which no goal state can be reached. We approach the problem from a generalized planning perspective and learn first-order-like formulas that characterize unsolvability for entire planning domains. We show how to cast the problem as a self-supervised classification task. Our training data is automatically generated and labeled by exhaustive exploration of small instances of each domain, and candidate features are automatically computed from the predicates used to define the domain. We investigate three learning algorithms with different properties and compare them to heuristics from the literature. Our empirical results show that our approach often captures important classes of unsolvable states with high classification accuracy. Additionally, the logical form of our heuristics makes them easy to interpret and reason about, and can be used to show that the characterizations learned in some domains capture exactly all unsolvable states of the domain.

Context-based segmentation of the longissimus muscle in beef with a deep neural network

2019 ◽  
Vol 28 (1) ◽  
pp. 47-57
Author(s):  
Karol Talacha ◽  
Izabella Antoniuk ◽  
Leszek Chmielewski ◽  
Michał Kruk ◽  
Jarosław Kurek ◽  
...  
Keyword(s):  
Neural Network ◽  
Cross Section ◽  
Cross Sections ◽  
Classification Accuracy ◽  
Training Data ◽  
Longissimus Muscle ◽  
Average Classification Accuracy ◽  
Beef Carcasses ◽  
Significant Difference

The problem of segmenting the cross-section through the longissimus muscle in beef carcasses with computer vision methods was investigated. The available data were 111 images of cross-sections coming from 28 cows (typically four images per cow). Training data were the pixels of the muscles, marked manually. The AlexNet deep convolutional neural network was used as the classifier, and single pixels were the classified objects. Each pixel was presented to the network together with its small circular neighbourhood, and with its context represented by the further neighbourhood, darkened by halving the image intensity. The average classification accuracy was 96\%. The accuracy without darkening the context was found to be smaller, with a small but statistically significant difference. The segmentation of the longissimus muscle is the introductory stage for the next steps of assessing the quality of beef for the alimentary purposes.

scholarly journals CAN THE CROWD DO THE JOB? EXPLORING THE EFFECTS OF INTEGRATING CUSTOMERS INTO A COMPANY’S BUSINESS MODEL INNOVATION

2016 ◽  
Vol 20 (07) ◽  
pp. 1650071 ◽  
Author(s):  
PHILIPP ALEXANDER EBEL ◽  
ULRICH BRETSCHNEIDER ◽  
JAN MARCO LEIMEISTER
Keyword(s):  
Business Models ◽  
Empirical Evaluation ◽  
Innovation Process ◽  
Business Model Innovation ◽  
High Quality ◽  
New Approach ◽  
New Business ◽  
Opening Up ◽  
New Business Models

While collaborative business modeling (CBM) constitutes a promising new approach for opening up a company’s innovation process, existing literature lacks empirical evidence of the effects related to this approach. Drawing on related literature on the quality of creative output, this paper proposes that in the context of a CBM initiative, the integration of customers will improve the quality of the generated output. As indicated by the results of our empirical evaluation, customers are indeed capable of developing high quality business models and are able to outperform company experts when it comes to the task of developing new business models.

Optimization of Diabetes Training DATA using Machine Learning Algorithms

2018 ◽  
Vol 6 (2) ◽  
pp. 283-286
Author(s):  
M. Samba Siva Rao ◽  
◽  
M.Yaswanth . ◽  
K. Raghavendra Swamy ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data

Guideline for the management of long tunnelled external ventricular drains in chronic hydrocephalus

2021 ◽  
Vol 30 (7) ◽  
pp. 416-421
Author(s):  
Phillip Correia Copley ◽  
John Emelifeonwu ◽  
Pasquale Gallo ◽  
Drahoslav Sokol ◽  
Jothy Kandasamy ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Nursing Care ◽  
Pilocytic Astrocytoma ◽  
Parental Education ◽  
Chronic Hydrocephalus ◽  
Treatment Protocols ◽  
New Approach ◽  
Viable Treatment

This article reports on the journey of a child with an inoperable hypothalamic-origin pilocytic astrocytoma causing hydrocephalus, which was refractory to treatment with shunts, and required a new approach. With multidisciplinary support, excellent nursing care and parental education, the child's hydrocephalus was managed long term in the community with bilateral long-tunnelled external ventricular drains (LTEVDs). This article describes the patient's journey and highlights the treatment protocols that were created to achieve this feat. Despite the difficulties in initially setting up these protocols, they proved successful and thus the team managing the patient proposed that LTEVDs are a viable treatment option for children with hydrocephalus in the context of inoperable tumours to help maximise quality of life.

Analyzing of UVLC system considering the effect of water depth

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Mustafa B. Al-Deen ◽  
Mazin Ali A. Ali ◽  
Zeyad A. Saleh
Keyword(s):  
Coastal Water ◽  
Water Depth ◽  
Bit Error Rate ◽  
Multiple Input Multiple Output ◽  
Visible Light Communications ◽  
New Approach ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Depth Water

Abstract This paper presents a new approach to discover the effect of depth water for underwater visible light communications (UVLC). The quality of the optical link was investigated with varying water depth under coastal water types. The performance of the UVLC with multiple input–multiple output (MIMO) techniques was examined in terms of bit error rate (BER) and data rate. The theoretical result explains that there is a good performance for UVLC system under coastal water.

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