An improved genetic programming technique for the classification of Raman spectra

Hennessy, Kenneth; Madden, Michael G.; Conroy, Jack; Ryder, Alan G.

doi:10.1016/j.knosys.2004.10.001

“…Previous publications have described some of the specific ML techniques that have been developed for this work [4,5,6,7]. This paper focuses on the system"s architecture and design, paying attention to features that facilitate its use by the target population of end users, who are typically analytical chemists without prior experience of using machine learning.…”

Section: Intensitymentioning

confidence: 99%

“…designed to optimise the assurance levels associated with discovered rules, so as to reduce the likelihood of misclassification of future samples [5].…”

Section: Improved Genetic Programming (Gp): This Technique Uses a Fitmentioning

confidence: 99%

See 1 more Smart Citation

A Machine Learning Application for Classification of Chemical Spectra

Madden

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Howley²

2009

Applications and Innovations in Intelligent Systems XVI

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Abstract. This paper presents a software package that allows chemists to analyze spectroscopy data using innovative machine learning (ML) techniques. The package, designed for use in conjunction with lab-based spectroscopic instruments, includes features to encourage its adoption by analytical chemists, such as having an intuitive graphical user interface with a step-by-step 'wizard' for building new ML models, supporting standard file types and data preprocessing, and incorporating well-known standard chemometric analysis techniques as well as new ML techniques for analysis of spectra, so that users can compare their performance. The ML techniques that were developed for this application have been designed based on considerations of the defining characteristics of this problem domain, and combine high accuracy with visualization, so that users are provided with some insight into the basis for classification decisions.

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“…In [2,188], binary classification is addressed by evolving a single threshold function with a fitness function based on classification accuracy and size penalty. A single threshold function is evolved in [71], where a fitness function includes two components: a classification accuracy and a measure of certainty. In [14,15,16], a single threshold discriminant function is evolved, but two special fitness functions are proposed to cope with class imbalance.…”

Section: Gp For Extracting Discriminant Functionsmentioning

confidence: 99%

Evolutionary Machine Learning for Classification with Incomplete Data

Tran¹

0

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<p>Classification is a major task in machine learning and data mining. Many real-world datasets suffer from the unavoidable issue of missing values. Classification with incomplete data has to be carefully handled because inadequate treatment of missing values will cause large classification errors. Existing most researchers working on classification with incomplete data focused on improving the effectiveness, but did not adequately address the issue of the efficiency of applying the classifiers to classify unseen instances, which is much more important than the act of creating classifiers. A common approach to classification with incomplete data is to use imputation methods to replace missing values with plausible values before building classifiers and classifying unseen instances. This approach provides complete data which can be then used by any classification algorithm, but sophisticated imputation methods are usually computationally intensive, especially for the application process of classification. Another approach to classification with incomplete data is to build a classifier that can directly work with missing values. This approach does not require time for estimating missing values, but it often generates inaccurate and complex classifiers when faced with numerous missing values. A recent approach to classification with incomplete data which also avoids estimating missing values is to build a set of classifiers which then is used to select applicable classifiers for classifying unseen instances. However, this approach is also often inaccurate and takes a long time to find applicable classifiers when faced with numerous missing values. The overall goal of the thesis is to simultaneously improve the effectiveness and efficiency of classification with incomplete data by using evolutionary machine learning techniques for feature selection, clustering, ensemble learning, feature construction and constructing classifiers. The thesis develops approaches for improving imputation for classification with incomplete data by integrating clustering and feature selection with imputation. The approaches improve both the effectiveness and the efficiency of using imputation for classification with incomplete data. The thesis develops wrapper-based feature selection methods to improve input space for classification algorithms that are able to work directly with incomplete data. The methods not only improve the classification accuracy, but also reduce the complexity of classifiers able to work directly with incomplete data. The thesis develops a feature construction method to improve input space for classification algorithms with incomplete data by proposing interval genetic programming-genetic programming with a set of interval functions. The method improves the classification accuracy and reduces the complexity of classifiers. The thesis develops an ensemble approach to classification with incomplete data by integrating imputation, feature selection, and ensemble learning. The results show that the approach is more accurate, and faster than previous common methods for classification with incomplete data. The thesis develops interval genetic programming to directly evolve classifiers for incomplete data. The results show that classifiers generated by interval genetic programming can be more effective and efficient than classifiers generated the combination of imputation and traditional genetic programming. Interval genetic programming is also more effective than common classification algorithms able to work directly with incomplete data. In summary, the thesis develops a range of approaches for simultaneously improving the effectiveness and efficiency of classification with incomplete data by using a range of evolutionary machine learning techniques.</p>

show abstract

“…In [2,188], binary classification is addressed by evolving a single threshold function with a fitness function based on classification accuracy and size penalty. A single threshold function is evolved in [71], where a fitness function includes two components: a classification accuracy and a measure of certainty. In [14,15,16], a single threshold discriminant function is evolved, but two special fitness functions are proposed to cope with class imbalance.…”

Section: Gp For Extracting Discriminant Functionsmentioning

confidence: 99%

Evolutionary Machine Learning for Classification with Incomplete Data

Tran¹

0

View full text Add to dashboard Cite

<p>Classification is a major task in machine learning and data mining. Many real-world datasets suffer from the unavoidable issue of missing values. Classification with incomplete data has to be carefully handled because inadequate treatment of missing values will cause large classification errors. Existing most researchers working on classification with incomplete data focused on improving the effectiveness, but did not adequately address the issue of the efficiency of applying the classifiers to classify unseen instances, which is much more important than the act of creating classifiers. A common approach to classification with incomplete data is to use imputation methods to replace missing values with plausible values before building classifiers and classifying unseen instances. This approach provides complete data which can be then used by any classification algorithm, but sophisticated imputation methods are usually computationally intensive, especially for the application process of classification. Another approach to classification with incomplete data is to build a classifier that can directly work with missing values. This approach does not require time for estimating missing values, but it often generates inaccurate and complex classifiers when faced with numerous missing values. A recent approach to classification with incomplete data which also avoids estimating missing values is to build a set of classifiers which then is used to select applicable classifiers for classifying unseen instances. However, this approach is also often inaccurate and takes a long time to find applicable classifiers when faced with numerous missing values. The overall goal of the thesis is to simultaneously improve the effectiveness and efficiency of classification with incomplete data by using evolutionary machine learning techniques for feature selection, clustering, ensemble learning, feature construction and constructing classifiers. The thesis develops approaches for improving imputation for classification with incomplete data by integrating clustering and feature selection with imputation. The approaches improve both the effectiveness and the efficiency of using imputation for classification with incomplete data. The thesis develops wrapper-based feature selection methods to improve input space for classification algorithms that are able to work directly with incomplete data. The methods not only improve the classification accuracy, but also reduce the complexity of classifiers able to work directly with incomplete data. The thesis develops a feature construction method to improve input space for classification algorithms with incomplete data by proposing interval genetic programming-genetic programming with a set of interval functions. The method improves the classification accuracy and reduces the complexity of classifiers. The thesis develops an ensemble approach to classification with incomplete data by integrating imputation, feature selection, and ensemble learning. The results show that the approach is more accurate, and faster than previous common methods for classification with incomplete data. The thesis develops interval genetic programming to directly evolve classifiers for incomplete data. The results show that classifiers generated by interval genetic programming can be more effective and efficient than classifiers generated the combination of imputation and traditional genetic programming. Interval genetic programming is also more effective than common classification algorithms able to work directly with incomplete data. In summary, the thesis develops a range of approaches for simultaneously improving the effectiveness and efficiency of classification with incomplete data by using a range of evolutionary machine learning techniques.</p>

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An improved genetic programming technique for the classification of Raman spectra

Cited by 19 publications

References 9 publications

A Machine Learning Application for Classification of Chemical Spectra

A Machine Learning Application for Classification of Chemical Spectra

Evolutionary Machine Learning for Classification with Incomplete Data

Evolutionary Machine Learning for Classification with Incomplete Data

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