Supervised classification techniques

Little has been reported on the use of hierarchical divisive methods for processing chemical data sets (other than the inclusion of the minimum-diameter method in some of the comparative studies mentioned above). Recursive partitioning, which is a supervised classification technique very closely related to monothetic divisive clustering, has, however, been used at the GlaxoSmithKline and Organon companies. 

Once the data are prepared, they can be explored chemometrically with techniques as PCA, rPCA, PP, and clustering. These enable visualization of the structure of the data set more specifically, they detect outliers and group similar samples. For several applications, it was confirmed that this approach outperforms the visual comparison of electropherograms. Chemometric techniques can also be apphed to classify samples based on their CE profile. When the classes in the data set are a priori known, supervised classification techniques as EDA, QDA, kNN, CART, PLSDA, SIMCA, and SVM can be used. The choice of techniques will often depend on the preference of the analyst and the complexity of the data. However, when nonlinear classification problems occur, a more complex technique as, for instance, SVM, will be outper-... 

Selection of the supervised classification technique or the combination of techniques suitable for accomplishing the classification task. Popular supervised classifiers are Multi-Layer Perceptron Artificial Neural Networks (MLP-ANN), Support Vector Machines (SVM), k-Nearest Neighbours (k-NN), combinations of genetic algorithms (GA) for feature selection with Linear Discriminant Analysis (LDA), Decision Trees and Radial Basis Function (RBF) classifiers. 

In general, several tools have been developed to match reference spectra with those measured by an imaging spectrometer. The most common approach is based on the use of standard supervised classification techniques, where known spectra are used to determine the statistical properties of each class based on spectral characteristics. Examples of supervised classification approaches applied to hyperspectral data are described in McKeown et al. (1999) and Roessner et al. (2001), where the maximum likelihood classifier (MLC) was applied to map urban land cover. Other techniques are based on the use of support vector machines (SVM) (Melgani and Bruzzone 2004) and neural networks (NNs) (Licciardi et al. 2009, 2012). Other approaches have been designed explicitly for the analysis of imaging spectrometry data, such as the Spectral Angle Mapper (SAM Kruse et al. 1993). 

Remote sensing techniques have been successfully applied for the identification of rocks in Cape Smith fold belt region. Principal Component Analysis is very effective for the separation of gabbro, metabasalt and peridotite. Band Ratio was helpful for the preliminary identification of peridotite. Supervised Classification approach is taken to verify the results obtained by Principal Component Analysis and Band Ratio. It is also useful to remap the unknown regions once the results are verified. 

Chen et al. (2008) employed a commercial electronic tongue, based on an array of seven sensors, to classify 80 green tea samples on the basis of their taste grade, which is usually assessed by a panel test. PCA was employed as an explorative tool, while fc-NN and a back propagation artificial neural network (BP-ANN) were used for supervised classification. Both the techniques provide excellent results, achieving 100% prediction ability on a test set composed of 40 samples (one-half of the total number). In cases like this, when a simple technique, such as fc-NN, is able to supply excellent outcomes, the utilization of a complex technique, like BP-ANN, does not appear justified from a practical point of view. 

The main objective of these techniques is to obtain, from the information of n = Y rii, genuine observations training set), classification rules that can be used to differentiate the k groups and to use these rules to assign new samples test set) into one of k groups. The most frequently used supervised classification methods... 

In complex systems where the number of groups to be separated during classification becomes larger, the performance of simple unsupervised methods (Section 3) degrades, requiring the use of more sophisticated supervised chemometric techniques. Additionally, in fields such a process NMR where there is a need for quantifying a component, the use of supervised methods becomes necessary. The different supervised methods described in the sections below have all been utilized in the chemometric analysis of NMR data for classification and/or quantitation. Examples utilizing these different techniques are discussed in Section 5. 

The aim of supervised classification is to create rules based on a set of training samples belonging to a priori known classes. Then the resulting rules are used to classify new samples in none, one, or several of the classes. Supervised pattern recognition methods can be classified as parametric or nonparametric and linear or nonlinear. The term parametric means that the method makes an assumption about the distribution of the data, for instance, a Gaussian distribution. Frequently used parametric methods are EDA, QDA, PLSDA, and SIMCA. On the contrary, kNN and CART make no assumption about the distribution of the data, so these procedures are considered as nonparametric. Another distinction between the classification techniques concerns the... 

A whole spectrum of statistical techniques have been applied to the analysis of DNA microarray data [26-28]. These include clustering analysis (hierarchical, K-means, self-organizing maps), dimension reduction (singular value decomposition, principal component analysis, multidimensional scaling, or correspondence analysis), and supervised classification (support vector machines, artificial neural networks, discriminant methods, or between-group analysis) methods. More recently, a number of Bayesian and other probabilistic approaches have been employed in the analysis of DNA microarray data [11], Generally, the first phase of microarray data analysis is exploratory data analysis. 

As a final remark it should be realized that when using some Supervised Learning techniques like SIMCA, the scaling of the data set is carried out only over the samples belonging to the same class (separate scaling). This is due because the own fundamentals of the methodology and has a beneficial effect on the classification (Derde et al., 1982). 

Clustering is a highly effective technique in data analysis. Prior to the introduction of clustering, it is important to understand the difference between clustering (imsuper-vised classification) and supervised classification. In supervised classification, we use traming objects with known class labels to develop a model and then deploy... 

Kotsiantis, S.B., Supervised machine learning A review of classification techniques, in Proceedings of the... 

A spectroscopic NIR imaging system, using a FPA detector, has been developed for remote and on-line measurements on a macroscopic scale. Multivariate statistical techniques are required to extract the important information from the multidimensional spectroscopic images. These techniques include PCA and linear discriminant analysis for supervised classification of spectroscopic image data (178). 

The decision tree classifier is chosen for its favorable tradeoff between performance and implementation simplicity. Classification using DT is a supervised learning technique, the input of the learning algorithm is a set of known data and the output is a tree model similar to the ones shown in Figure 5. Once the tree is defined, the classification of new inputs starts at the root decision node of the tree and terminates at one of the leaf nodes that represent a specific class, passing by intermediate decision nodes. 

Kotsiantis SB (2007) Supervised machine learning a review of classification techniques. Informatica 31 249-268... 

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