Modeling of laser-induced breakdown spectroscopic data analysis by an automatic classifier

Laser-induced breakdown spectroscopy (LIBS) is a multi-elemental and real-time analytical technique with simultaneous detection of all the elements in any type of sample matrix including solid, liquid, gas, and aerosol. LIBS produces vast amount of data which contains information on elemental composition of the material among others. Classification and discrimination of spectra produced during the LIBS process are crucial to analyze the elements for both qualitative and quantitative analysis. This work reports the design and modeling of optimal classifier for LIBS data classification and discrimination using the apparatus of statistical theory of detection. We analyzed the noise sources associated during the LIBS process and created a linear model of an echelle spectrograph system. We validated our model based on assumptions through statistical analysis of “dark signal” and laser-induced breakdown spectra from the database of National Institute of Science and Technology. The results obtained from our model suggested that the quadratic classifier provides optimal performance if the spectroscopy signal and noise can be considered Gaussian

An Empirical Study on Content Analysis Use in Test and Evaluation Deficiency Report Analysis

This research investigated strategies and heuristics used to prioritize system deficiencies identified during test and evaluation. Five participants were recruited to participate in this laboratory study and were assigned to an experiment condition either with or without content analysis training. Content analysis is a well-known methodology for identifying patterns and themes in qualitative datasets. In either experiment condition, subjects were asked to (1) classify a set of flight simulator deficiencies, (2) develop a deficiency resolution priority order using those classifications, and (3) complete a set of questionnaires regarding the completion of these tasks and demographic information. Across the five subjects, there was fairly high variability in the strategies and methods used. Therefore, the impact of the content analysis training was inconclusive. However, the variety of observed approaches warrants future research, specifically into the use of multiple categorization schemes when deciding upon a deficiency resolution priority order.Naval Postgraduate School Acquisition Research Progra

A statistical approach to improving accuracy in classifier ensembles

Popular ensemble classifier induction algorithms, such as bagging and boosting, construct the ensemble by optimizing component classifiers in isolation. The controllable degrees of freedom in an ensemble include the instance selection and feature selection for each component classifier. Because their degrees of freedom are uncoupled, the component classifiers are not built to optimize performance of the ensemble, rather they are constructed by minimizing individual training loss. Recent work in the ensemble literature contradicts the notion that a combination of the best individually performing classifiers results in lower ensemble error rates. Zenobi et al. demonstrated that ensemble construction should consider a classifier\u27s contribution to ensemble accuracy and diversity even at the expense of individual classifier performance. To tradeoff individual accuracy against ensemble accuracy and diversity, a component classifier inducer requires knowledge of the choices made by the other ensemble members. We introduce an approach, called DiSCO, that exercises direct control over the tradeoff between diversity and error by sharing ensemble-wide information on instance selection during training. A classifier\u27s contribution to ensemble accuracy and diversity can be measured as it is constructed in isolation, but without sharing information among its peers in the ensemble during training, nothing can be done to control it. In this work, we explore a method for training the component classifiers collectively by sharing information about training set selection. This allows our algorithm to build ensembles whose component classifiers select complementary error distributions that maximize diversity while minimizing ensemble error directly. Treating ensemble construction as an optimization problem, we explore approaches using local search, global search and stochastic methods. Using this approach we can improve ensemble classifier accuracy over bagging and boosting on a variety of data, particularly those for which the classes are moderately overlapping. In ensemble classification research, how to use diversity to build effective classifier teams is an open question. We also provide a method that uses entropy as a measure of diversity to train an ensemble classifier

An Empirical Study on Content Analysis Use in Test and Evaluation Deficiency Report Analysis

This research investigated strategies and heuristics used to prioritize system deficiencies identified during test and evaluation. Five participants were recruited to participate in this laboratory study, and were assigned to an experiment condition either with or without content analysis training. Content analysis is a well-known methodology for identifying patterns and themes in qualitative datasets. In either experiment condition, subjects were asked to (1) classify a set of flight simulator deficiencies, (2) develop a deficiency resolution priority order using those classifications, and (3) complete a set of questionnaires regarding the completion of these tasks and demographic information. Across the five subjects, there was fairly high variability in the strategies and methods used. Therefore, the impact of the content analysis training was inconclusive. However, the variety of observed approaches warrants future research, specifically into the use of multiple categorization schemes when deciding upon a deficiency resolution priority order.Naval Postgraduate School Acquisition Research Progra

Interactive Learning Using Manifold Geometry

We present an interactive learning method that enables a user to iteratively refine a regression model. The user examines the output of the model, visualized as the vertical axis of a 2D scatterplot, and provides corrections by repositioning individual data instances to the correct output level. Each repositioned data instance acts as a control point for altering the learned model, using the geometry underlying the data. We capture the underlying structure of the data as a manifold, on which we compute a set of basis functions as the foundation for learning. Our results show that manifold-based interactive learning improves performance monotonically with each correction, outperforming alternative approaches

Training ensembles using max-entropy error diversity

Ensembles provide a powerful method for improving the performance of automated classifiers by constructing piecewise models that combine individual component classifier hypotheses. Together, the combined output of the component classifiers is more capable of fitting the type of complex decision boundaries in data sets where class boundaries overlap and class exemplars are disperse in feature space. A key ingredient to ensemble classifier induction is error diversity among component classifiers. Work in the ensemble literature suggests that ensemble construction should consider diversity even at some expense to individual classifier performance. To make such tradeoffs, a component classifier inducer requires knowledge of the choices made by its peers in the ensemble. In this work, we present a method called MaxEnt-DiSCO that trains component classifiers collectively using entropy as a measure of error diversity. Using the maximum entropy framework, we share information on instance selection among component classifiers collectively during training. This allows us to train component classifiers collectively so that their errors are maximally diverse. Experiments demonstrate the utility of our approach for data sets where the classes have a moderate degree of overlap. © 2009 American Institute of Physics

An Empirical Study on Content Analysis Use in Test and Evaluation Deficiency Report Analysis

This research investigated strategies and heuristics used to prioritize system deficiencies identified during test and evaluation. Five participants were recruited to participate in this laboratory study and were assigned to an experiment condition either with or without content analysis training. Content analysis is a well-known methodology for identifying patterns and themes in qualitative datasets. In either experiment condition, subjects were asked to (1) classify a set of flight simulator deficiencies, (2) develop a deficiency resolution priority order using those classifications, and (3) complete a set of questionnaires regarding the completion of these tasks and demographic information. Across the five subjects, there was fairly high variability in the strategies and methods used. Therefore, the impact of the content analysis training was inconclusive. However, the variety of observed approaches warrants future research, specifically into the use of multiple categorization schemes when deciding upon a deficiency resolution priority order.Naval Postgraduate School Acquisition Research Progra

A family of Chisini mean based Jensen-Shannon divergence kernels

Jensen-Shannon divergence is an effective method for measuring the distance between two probability distributions. When the difference between these two distributions is subtle, Jensen-Shannon divergence does not provide adequate separation to draw distinctions from subtly different distributions. We extend Jensen-Shannon divergence by reformulating it using alternate operators that provide different properties concerning robustness. Furthermore, we prove a number of important properties for this extension: the lower limits of its range, and its relationship to Shannon Entropy and Kullback-Leibler divergence. Finally, we propose a family of new kernels, based on Chisini mean Jensen-Shannon divergence, and demonstrate its utility in providing better SVM classification accuracy over RBF kernels for amino acid spectra. Because spectral methods capture phenomenon at subatomic levels, differences between complex compounds can often be subtle. While the impetus behind this work began with spectral data, the methods are generally applicable to domains where subtle differences are important

Investigating manifold neighborhood size for nonlinear analysis of LIBS amino acid spectra

Classification and identification of amino acids in aqueous solutions is important in the study of biomacromolecules. Laser Induced Breakdown Spectroscopy (LIBS) uses high energy laser-pulses for ablation of chemical compounds whose radiated spectra are captured and recorded to reveal molecular structure. Spectral peaks and noise from LIBS are impacted by experimental protocols. Current methods for LIBS spectral analysis achieves promising results using PCA, a linear method. It is well-known that the underlying physical processes behind LIBS are highly nonlinear. Our work set out to understand the impact of LIBS spectra on suitable neighborhood size over which to consider pattern phenomena, if nonlinear methods capture pattern phenomena with increased efficacy, and how they improve classification and identification of compounds. We analyzed four amino acids, polysaccharide, and a control group, water. We developed an information theoretic method for measurement of LIBS energy spectra, implemented manifold methods for nonlinear dimensionality reduction, and found while clustering results were not statistically significantly different, nonlinear methods lead to increased classification accuracy. Moreover, our approach uncovered the contribution of micro-wells (experimental protocol) in LIBS spectra. To the best of our knowledge, ours is the first application of Manifold methods to LIBS amino-acid analysis in the research literature