Learning classification techniques

Partitioning methods occasionally struggle to provide the accuracy associated with more powerful, albeit less informative techniques such as machine learning and statistical approaches. For this reason, there is a continuing need for the application of more accurate and informative classification techniques to QSAR analysis. The goal of a classifier is to produce a model that can separate new, untested compounds into classes with a training set of already classified compounds. 

Discriminant analysis techniques (also called classification techniques) are concerned with classifying objects into one of two or more classes. Discriminant techniques are considered to be learning procedures. Given, a set of objects whose class identity is known, a model learns from the variables which have been measured for each of the objects, a procedure which can be used to assign a new object, whose class identity is unknown, into one of the predefined classes. Such a procedure is performed using a well-defined discriminatory rule. 

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

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

Supervised Learning. Supervised learning refers to a collection of techniques ia which a priori knowledge about the category membership of a set of samples is used to develop a classification rule. The purpose of the rule is usually to predict the category membership for new samples. Sometimes the objective is simply to test the classification hypothesis by evaluating the performance of the rule on the data set. 

We will explore the two major families of chemometric quantitative calibration techniques that are most commonly employed the Multiple Linear Regression (MLR) techniques, and the Factor-Based Techniques. Within each family, we will review the various methods commonly employed, learn how to develop and test calibrations, and how to use the calibrations to estimate, or predict, the properties of unknown samples. We will consider the advantages and limitations of each method as well as some of the tricks and pitfalls associated with their use. While our emphasis will be on quantitative analysis, we will also touch on how these techniques are used for qualitative analysis, classification, and discriminative analysis. 

Inductive learning by decision trees is a popular machine learning technique, particularly for solving classification problems, and was developed by Quinlan (1986). A decision tree depicting the input/output mapping learned from the data in Table I is shown in Fig. 22. The input information consists of pressure, temperature, and color measurements of... 

One way to develop an in silica tool to predictive promiscuity is to apply a NB classifier for modeling, a technique that compares the frequencies of features between selective and promiscuous sets of compounds. Bayesian classification was applied in many studies and was recently compared to other machine-learning techniques [26, 27, 43, 51, 52]. 

Fig. 9-1 shows the formation of four clusters which are preserved even in the case of varied similarity over a larger range (30%). The local distribution of cluster points is presented in Fig. 9-2. This hypothesis found with the unsupervised learning technique is checked in the following section by means of multidimensional classification.

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