Classification algorithm

Van Veldhuizen, D. A. (1999). Multiobjective Evolutionary Algorithms Classifications, Analyses, and New Innovations, Ph.D. thesis. Department of Electrical and Computer Engineering. Air Force Institute of Technology, Wright-Patterson AFB, Ohio. 

Several aspects of logic algorithm design can be discussed now. We first propose a useful terminology for logic algorithm classification, and then show in what sense minimal cases and non-recursive non-minimal cases, though syntactically similar, are totally different concepts. 

K. L. Clark and J. Darlington. Algorithm classification through synthesis. The Computer Journal 23(l) 61-65,1980. 

The evaluation of the proposed algorithms for the classification of ultrasonic resonance spectra clearly indicated that an adequate classifier can in many practical cases be obtained by... 

The initial classification model of dispersion properties of engineering materials was obtained The algorithm of its creation includes ... 

A flaw characterisation module (not described here) has also been developed this module uses a number of classification algorithms and various features of the stored RF signals to make a judgement of the type of flaw present. 

The Morgan Algorithm classifies all the congeneric atoms of a compound and selects invariant-labeled atoms (see Section 2.5.3.1). The classification uses the concept of considering the number of neighbors of an atom (connectivity), and does so in an iterative manner (extended connectivity, EC). On the basis of certain rules. 

To be able to follow some algorithmic approaches to reaction classification... 

One can find more details on the algorithm in Section 4.3.4. This time the learning yielded essentially improved results. It is sufficient to say that if in the case of the primary dataset, only 21 compoimds from 91 were classified correctly, whereas in the optimized dataset (i.e., that with no redundancy) the correctness of classification was increased to 65 out of 91. 

Other methods consist of algorithms based on multivariate classification techniques or neural networks they are constructed for automatic recognition of structural properties from spectral data, or for simulation of spectra from structural properties [83]. Multivariate data analysis for spectrum interpretation is based on the characterization of spectra by a set of spectral features. A spectrum can be considered as a point in a multidimensional space with the coordinates defined by spectral features. Exploratory data analysis and cluster analysis are used to investigate the multidimensional space and to evaluate rules to distinguish structure classes. 

FI Spath. Cluster-Analysis Algorithms for Data Reduction and Classification of Objects. Chichester Ellis Florwood, 1980. 

Natsoulis G, El Ghaoui L, Lanckriet GR, Tolley AM, Leroy F, Dunlea S, et al. Classification of a large microarray data set algorithm comparison and analysis of drug signatures. Genome Res 2005 15 724-36. 

When applied to QSAR studies, the activity of molecule u is calculated simply as the average activity of the K nearest neighbors of molecule u. An optimal K value is selected by the optimization through the classification of a test set of samples or by the leave-one-out cross-validation. Many variations of the kNN method have been proposed in the past, and new and fast algorithms have continued to appear in recent years. The automated variable selection kNN QSAR technique optimizes the selection of descriptors to obtain the best models [20]. 

The algorithm that we employ to build a classification decision tree from (x, y) data records belongs to a group of techniques known as top-down induction of decision trees (TDIDT) (Sonquist et al., 1971 Fu, 1968 Hunt, 1962 Quinlan, 1986, 1987, 1993 Breiman et al., 1984). 

Hunt, E., Concept Learning An Information Processing Problem. Wiley, New York, 1962. James, M., Classification Algorithms. Wiley, New York, 1985. 

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