Kernel-based techniques (such as support vector machines, Bayes point machines, kernel principal component analysis, and Gaussian processes) represent a major development in machine learning algorithms. Support vector machines (SVM) are a group of supervised learning methods that can be applied to classification or regression. In a short period of time, SVM found numerous applications in chemistry, such as in drug design (discriminating between ligands and nonligands, inhibitors and noninhibitors, etc.), quantitative structure-activity relationships (QSAR, where SVM regression is used to predict various physical, chemical, or biological properties), chemometrics (optimization of chromatographic separation or compound concentration prediction from spectral data as examples), sensors (for qualitative and quantitative prediction from sensor data), chemical engineering (fault detection and modeling of industrial processes), and text mining (automatic recognition of scientific information). Support vector machines represent an extension to nonlinear models of the generalized portrait algorithm developed by Vapnik and Lerner. The SVM algorithm is based on the statistical learning theory and the Vapnik–Chervonenkis

Applications of Support Vector Machines in Chemistry

Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM

A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach

Identification of flow regime in vertical upward air–water pipe flow using differential pressure signals and elastic maps

The main goal of the new field of data mining is the analysis of large and complex datasets. Some very important datasets may be derived from business and industrial activities. This kind of data is known as enterprise data . The common characteristic of such datasets is that the analyst wishes to analyze them for the purpose of designing a more cost-effective strategy for optimizing some type of performance measure, such as reducing production time, improving quality, eliminating wastes, or maximizing profit. Data in this category may describe different scheduling scenarios in a manufacturing environment, quality control of some process, fault diagnosis in the operation of a machine or process, risk analysis when issuing credit to applicants, management of supply chains in a manufacturing system, or data for business related decision-making. Contents: Enterprise Data Mining: A Review and Research Directions (T W Liao); Application and Comparison of Classification Techniques in Controlling Credit Risk (L Yu et al.); Predictive Classification with Imbalanced Enterprise Data (S Daskalaki et al.); Data Mining Applications of Process Platform Formation for High Variety Production (J Jiao & L Zhang); Multivariate Control Charts from a Data Mining Perspective (G C Porzio & G Ragozini); Maintenance Planning Using Enterprise Data Mining (L P Khoo et al.); Mining Images of Cell-Based Assays (P Perner); Support Vector Machines and Applications (T B Trafalis & O O Oladunni); A Survey of Manifold-Based Learning Methods (X Huo et al.); and other papers.

Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications

This paper presents a novel method for the classification of vertical and horizontal two-phase flow regimes in pipes based on a multiclass support vector machine model. Using previously published experimental data for gas−liquid vertical and horizontal two-phase flows, the goal of the model is to predict the transition region between the flow regimes. The transition region is determined with respect to pipe diameter, superficial gas velocity, and superficial liquid velocity. The support vectors of these data are identified and used to determine the transition zone between the multiphase flow patterns. The model proved to be an accurate classification tool for the identification of two-phase flow regimes in pipes. Our computational results show that flow regime predictions from the MSVM models are generally more accurate than predictions based on theoretical correlations.

Two-phase flow regime identification with a multiclassification support vector machine (SVM) model

We present a knowledge-based linear multi-classification model for vertical two-phase flow regimes in pipes with the transition equations of McQuillan & Whalley [1] used as prior knowledge. Using published experimental data for gas-liquid vertical two-phase flows, and expert domain knowledge of the two-phase flow regime transitions, the goal of the model is to identify the transition region between different flow regimes. The prior knowledge is in the form of polyhedral sets belonging to one or more classes. The resulting formulation leads to a Tikhonov regularization problem that can be solved using matrix or iterative methods.

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Knowledge-Based multiclass support vector machines applied to vertical two-phase flow

A regularized pairwise multi-classification knowledge-based machine and applications

The binary support vector machines (SVMs) have been extensively investigated. However their extension to a multi-classification model is still an on-going research. In this paper we present an extension of the binary support vector machines (SVMs) for the k > 2 class problems. The SVM model as originally proposed requires the construction of several binary SVM classifiers to solve the multi-class problem. We propose a single quadratic optimization problem called a pairwise multi-classification support vector machines (PAMSVMs) for constructing a pairwise linear and nonlinear classification decision functions. A kernel approach is also discussed for nonlinear classification problems. Computational results are presented for two real data sets.

http://www.wseas.us/e-library/conferences/2005lisbon/papers/496-222.pdf?origin=publication_detail

Pairwise multi-classification support vector machines: quadratic programming (QP-P A MSVM) formulations

This paper presents least square formulations for constructing a pair-wise linear and nonlinear classification decision functions. The formulations are based on the KKT system obtained from the optimality conditions of the PAMSVM problem. This derivation can be considered a variant of the Suykens and Vandewalle's least square multi-class SVMs, with the notable difference been the equality constraints used in their problem formulation and the encoded classes (labels) represented by multiple streams for the system output. The least square formulation will be of two types namely the pairwise least square multi-classification support vector machine (PALS-MSVM) and piecewise least square multi-classification support vector machine (PILS-MSVM). A piece-wise MSVM formulation will be selected from the existing literature and its optimality conditions will be written out and expressed as a least square problem. The structures of both LS problems are essentially the same with the only difference been the matrix which has the dataset information. Since the Mercer conditions are applicable, kernels can be implemented as appropriate for nonlinear classification problems.

Olutayo O. Oladunni

Papers

Two-phase flow regime identification with a multiclassification support vector machine (SVM) model

Knowledge-Based multiclass support vector machines applied to vertical two-phase flow

A regularized pairwise multi-classification knowledge-based machine and applications

Pairwise multi-classification support vector machines: quadratic programming (QP-P A MSVM) formulations

Least square multi-classification support vector machines: pair-wise (P A LS-MSVM) & piece-wise (P I LS-MSVM) formulations