Performance Evaluation of One-Class Classification-based Control Charts through an Industrial Application

Summary This research addresses the problem of prediction of protein-protein interactions (PPI) when integrating diverse kinds of biological information. This task has been commonly viewed as a binary classification problem (whether any two proteins do or do not interact) and several different machine learning techniques have been employed to solve this task. However the nature of the data creates two major problems which can affect results. These are firstly imbalanced class problems due to the number of positive examples (pairs of proteins which really interact) being much smaller than the number of negative ones. Secondly the selection of negative examples can be based on some unreliable assumptions which could introduce some bias in the classification results.Here we propose the use of one-class classification (OCC) methods to deal with the task of prediction of PPI. OCC methods utilise examples of just one class to generate a predictive model which consequently is independent of the kind of negative examples selected; additionally these approaches are known to cope with imbalanced class problems. We have designed and carried out a performance evaluation study of several OCC methods for this task, and have found that the Parzen density estimation approach outperforms the rest. We also undertook a comparative performance evaluation between the Parzen OCC method and several conventional learning techniques, considering different scenarios, for example varying the number of negative examples used for training purposes. We found that the Parzen OCC method in general performs competitively with traditional approaches and in many situations outperforms them. Finally we evaluated the ability of the Parzen OCC approach to predict new potential PPI targets, and validated these results by searching for biological evidence in the literature.

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A One-Class Classification-Based Control Chart Using the K-Means Data Description Algorithm

Journal of Quality and Reliability Engineering ◽

10.1155/2014/239861 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9

Author(s):

Walid Gani ◽

Mohamed Limam

Keyword(s):

Control Chart ◽

Control Charts ◽

Nearest Neighbor ◽

Average Run Length ◽

Control Sample ◽

Control Process ◽

Process Data ◽

Data Description ◽

Kernel Distance ◽

One Class Classification

This paper aims to enlarge the family of one-class classification-based control charts, referred to as OC-charts, and extend their applications. We propose a new OC-chart using the K-means data description (KMDD) algorithm, referred to as KM-chart. The proposed KM-chart gives the minimum closed spherical boundary around the in-control process data. It measures the distance between the center of KMDD-based sphere and the new incoming sample to be monitored. Any sample having a distance greater than the radius of KMDD-based sphere is considered as an out-of-control sample. Phase I and II analysis of KM-chart was evaluated through a real industrial application. In a comparative study based on the average run length (ARL) criterion, KM-chart was compared with the kernel-distance based control chart, referred to as K-chart, and the k-nearest neighbor data description-based control chart, referred to as KNN-chart. Results revealed that, in terms of ARL, KM-chart performed better than KNN-chart in detecting small shifts in mean vector. Furthermore, the paper provides the MATLAB code for KM-chart, developed by the authors.

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One-Class Classification-Based Control Charts for Monitoring Autocorrelated Multivariate Processes

Communications in Statistics - Simulation and Computation ◽

10.1080/03610910903480826 ◽

2010 ◽

Vol 39 (3) ◽

pp. 461-474 ◽

Cited By ~ 11

Author(s):

Seoung Bum Kim ◽

Weerawat Jitpitaklert ◽

Thuntee Sukchotrat

Keyword(s):

Control Charts ◽

Multivariate Processes ◽

One Class Classification

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Performance evaluation of producer gas burner for industrial application

Biomass and Bioenergy ◽

10.1016/j.biombioe.2010.12.046 ◽

2011 ◽

Vol 35 (3) ◽

pp. 1373-1377 ◽

Cited By ~ 10

Author(s):

N.L. Panwar ◽

B.L. Salvi ◽

V. Siva Reddy

Keyword(s):

Performance Evaluation ◽

Industrial Application ◽

Producer Gas ◽

Gas Burner

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Control loop performance assessment and improvement of an industrial hydrotreating unit and its economical benefits

Sba Controle & Automação Sociedade Brasileira de Automatica ◽

10.1590/s0103-17592012000100006 ◽

2012 ◽

Vol 23 (1) ◽

pp. 60-77 ◽

Cited By ~ 1

Author(s):

L.G.S. Longhi ◽

A.S. Reginato ◽

H.C.G. Teixeira ◽

C.A.C. Cortez ◽

L.P. Lusa ◽

...

Keyword(s):

Performance Evaluation ◽

Performance Assessment ◽

Industrial Application ◽

Evaluation Methods ◽

Regulatory Control ◽

Control Performance ◽

Control Loop ◽

Control Loop Performance

This paper describes the CLPA (Control Loop Performance Assessment) and its improvement applied to an industrial hydrotreating unit (HDT) located at Alberto Pasqualini Refinery (REFAP S.A.). It is presented a review on regulatory control performance evaluation methods and the developed tool is described. The basic steps of the work methodology are also presented, with focus on the planned and implanted actions to improve the unit dynamical performance. Finishing the paper, the main results for a long-term industrial application are presented, including the unusual economic gains evaluation.

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