Rule extraction from data

Figure 6 shows a structure of applying fuzzy logic in control. First, two types of inputs must be obtained numerical inputs and human knowledge or rule extraction from data (i.e., fuzzy rules). Then the numerical inputs must be fuzzified into fuzzy numbers. The fuzzy rules consist of the fuzzy membership functions (knowledge model) or so-called fuzzy associative memories (FAMs). Then the... 

Human ICnowledge or Rule Extraction from Data... 

In this rule (as well as in all important kinds of logical rules extracted from data), three parts with different semantics can be distinguished ... 

Holena, M. (2006). Piecewise-linear neural networks and their relationship to rule extraction from data. Neural Comput, 18, 2813-2853. 

Rule extraction from a mutagenicity data set using adaptively grown phylogenetic-like trees../. Chem. Inf. Comput. Sci. 42, 1104—1 111. 

Bacha, P.A., Gruver, H.S., Den Hartog, B.K., Tamura, S.Y. and Nutt, R.E. (2002) Rule extraction from a mutagenicity data set using adaptively grown phylogenetic-like trees. J. Chem. Inf. Comput. Sci., 42, 1104-1111. 

Another important factor that influences on the accuracy of models is a metabolic pathway or metabohc activation of the parent compound which is not usually considered. It would be reasonable to predict the first reactive metabolite or find an actnal proof that it is produced, and then make a separate prediction for the metabolite of interest. Several computer programs provide either the prediction of htunan metabolism or common metabolic pathway based on the rules extracted from several species of mammals (rat, mouse, hitman, hamster), using a mixture of in vitro and in vivo metabohsm data of compoimds [68, 89]. However, in these programs, predictions made for the possible reactive metabolites do not affect the prediction for a parent compoimd. 

Richards, Meyer and Packard [richa90] have suggested a way to extract two-dimensional cellular automaton rules directly from experimental data. The same idea, outlined below, can in principle be used in more general contexts. 

IC5o=1.0pM, MW=294, HA=22). Open circles indicate compounds with R05>1 (at least one parameter from Lipinski s Rule-of 5 is out of range). Activity data extracted from GVKBIO. 

The bottleneck of phenomenological models is the large number of independent parameters (27 in low-symmetric complexes) required for the description of the CF, which cannot be reliably extracted from experiment in a unique manner. As a rule, these models are confronted with the description of a limited amount of experimental data, while it is not possible in principle to provide the entire set of CF parameters. The latter strongly depend on fitted experiments and, therefore, are not reliable (an example is described below). 

They vary from discipline to discipline and from time to time. As might be expected, although appropriate in the context of their development, such standards are often likely to be incomplete or otherwise inappropriate for universal application because they are value-laden rules for making choices (vide infra). Through use, however, they become valuable to those who use them. Often these standards also become a cause of contention when, in interdisciplinary settings, practical knowledge must be extracted from experimental data. Thus, controversies often arise when scientists trained in different disciplines influence public policies or make decisions based on the conversion standards in which they were trained. 

What are the orientation rules than can be extracted from the data obtained with various aromatic compounds and nucleophiles ... 

In concluding this section on the influence of the nature of the nucleophile, it is important to stress the dominant influence of the nucleophile on the stereochemistry at silicon. This effect cannot be interpreted in terms of the stability of the intermediate on the basis of the apicophilic-ity rule as stated in phosphorus chemistry. It fails to explain the retention of configuration as stereochemical outcome. No better explanation can be extracted from the quasicyclic SNi-Si mechanism (/. 2). On the other hand, data obtained with various nucleophiles show clearly that the stereochemistry is controlled by the electronic character of the nucleophile. In other words, this factor at first determines the geometry of attack of the nucleophile at silicon, which leads in a first determinant step to the formation of a pentacoordinate intermediate (55). We proposed the following ... 

Irrespective of the origin of fractals or fractal-like behavior in experimental studies, the investigator has to derive an estimate for df from the data. Since strict self-similarity principles cannot be applied to experimental data extracted from irregularly shaped objects, the estimation of df is accomplished with methods that unveil either the underlying replacement rule using self-similarity principles or the power-law scaling. Both approaches give identical results and they will be described briefly. 

Process control and model identification. Simultaneous classification may be useful for control model identification and for extraction of the rules of control from data. 

Figure 14 exemplifies two computational methods to determine the probability distribution of composition for binary polymer blends described by the bond fluctuation model [67]. Phase coexistence can be extracted from these data via the equal-weight rule. For the specific example of a symmetric blend, the coexistence value of the exchange chemical potential, A/u, is dictated by the S3munetry. One can simply simulate at A oex = 0 and monitor the composition. Nevertheless, the probability distribution contains additional information, as discussed in Sect. 3.5. 

In general, the decoding reliability can be improved by decoding an entire watermark letter sequence d, where the known encoding of m into d can be exploited to estimate the most likely d, or equivalently, to estimate the most likely watermark message m. The simple codebook structure of SCS can be exploited to efficiently estimate d. First, data y is extracted from the received data r. This extraction process operates sample-wise, where the extraction rule for the nth element is... 

---