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the commercial sector is indoor temperature control and lighting. Industrial load results from electricity requirements of manufacturing processes. Load drivers affect commercial and industrial load as follows. Population Commercial and industrial activity increase with increasing population. Accordingly, as with residential load, commercial and industrial load increases with increasing population. GDP Commercial and industrial load is increased or decreased by strong or adverse economic growth. This follows from an overall increase or decrease in commercial and industrial activity associated with corresponding strengthening or weakening of the GDP. Temporal Behavior Patterns As with residential load, commercial activity has seasonal, daily, and hourly patterns that affect load. These patterns are highly predictable as commercial operations work on a strict time clock. Manufacturing process follow strict and planned timelines. Industrial load follows the processing timelines and is very predictable. Weather Parameters As indoor temperature control is a sizable component of commercial load, weather impacts are similar to those on residential load. However, commercial buildings are generally better constructed than residential buildings; accordingly, they are not as sensitive to weather changes as residential buildings. With the exception of extreme weather such as hurricanes, industrial load is not sensitive to weather conditions.

Finally, the phone call databases to learn from are very large (typically they have 4 6 million records and 600 800 million bytes) and missing values are very common Some learning methods cannot handle such volumes of data in reasonable time, because they may have to process the data several times to reach a solution (for example, artificial neural networks) Missing values also pose severe problems for some classifier induction methods A possible solution, which nicely handles most of the difficulties pointed out above, is the application of a naive Bayes classifier (cf Section 61) A naive Bayes classifier can cope with the unequal class distribution and the unequal misclassification costs, since it only computes class probabilities The threshold probability at which an action is taken wrt.

In looking at surveys, the organization then matched job titles and job content to comparable data other companies that were of similar size and scope JC Penney targeted the 50th percentile of the pay ranges for each job Career Bands The jobs were then grouped by relative impact to the organization to create career bands, each with a maximum and minimum pay range This reduced the number of titles while still maintaining logical career paths for employees That structure.

a possibly fraudulent costumer can be fixed later, depending, for instance, on an analysis of expected loss based on test data A naive Bayes classifier can also exploit at least part of the information conveyed by any of the attributes, since the attributes are treated independently and thus using one attribute does not restrict using any other However, the main drawback of a naive Bayes classifier, namely the strong independence assumptions, leads to suboptimal performance in this application Therefore it is advisable not to rely on a pure naive Bayes classifier, but to augment it with edges between some of the descriptive attributes If appropriate heuristics are used to select these edges, it is possible to retain the efficiency with which a naive Bayes classifier can be constructed, thus enabling this approach to handle large volumes of data.

The APRI (Advanced Pattern Recognition and Identification) system developed at AT&T [Ezawa and Norton 1995, Ezawa et al 1996] was designed according to the general scheme outlined above It builds a Bayesian network classifier starting from a naive Bayes structure and employs heuristics based on mutual information (cf Sections 724 and 732) to select the attributes used in the classifier and the additional edges between them The Bayesian network construction in APRI has four phases In the first phase the database is scanned to determine the domains of all attributes This phase may involve a discretization of continuous attributes using an information-based approach In the second phase the descriptive attributes to be used in the classifier are selected To this end, the mutual information of the class attribute C and each descriptive attribute A is computed.

presenting complaint of the clients and examination of how they are functioning in accordance with the five Cs, exploration is made of what keeps them jointly and individually from fulfilling the marital tasks Sometimes, the actions of one or both partners are so transparent that it is clear from the outset that adequate commitment is missing CA SE ST U DY Albert, for example, met Janet while he was on an extended work assignment in her small hometown She pushed for marriage from early in their contact They scheduled their wedding for shortly after Albert completed the assignment, and they began their married life in the large city where his corporate employer was located.

Then a minimal set of attributes M U (where U is the set of all descriptive attributes) is determined, which satisfies Imutual (C, A).

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