An overview of reconstructability analysis <s> BHLNORUX <s> We consider the problem of matching domain-specific statistical structure to neural-network (NN) architecture. In past work we have considered this problem in the function approximation context; here we consider the pattern classification context. General Systems Methodology tools for finding problem-domain structure suffer exponential scaling of computation with respect to the number of variables considered. Therefore we introduce the use of Extended Dependency Analysis (EDA), which scales only polynomially in the number of variables, for the desired analysis. Based on EDA, we demonstrate a number of NN pre-structuring techniques applicable for building neural classifiers. An example is provided in which EDA results in significant dimension reduction of the input space, as well as capability for direct design of an NN classifier. <s> BIB001 </s> An overview of reconstructability analysis <s> BHLNORUX <s> Employers of hourly workers typically experience high employee turnover. Due to costs associated with: training, hiring and termination, the overhead from this high turnover rate is substantial. It is therefore desirable to construct employee selection procedures and analytic models to estimate the likely tenure of applicants for employment prior to a hiring decision. A critical component in the success of this effort to create a neural network model to estimate tenure was the application of information-theoretic feature selection. The benefits of this technique are demonstrated by comparison with results obtained using no feature selection and alternate methods of feature selection. <s> BIB002 </s> An overview of reconstructability analysis <s> BHLNORUX <s> Extended dependency analysis (EDA) is a heuristic search technique for finding significant relationships between nominal variables in large data sets. The directed version of EDA searches for maximally predictive sets of independent variables with respect to a target dependent variable. The original implementation of EDA was an extension of reconstructability analysis. Our new implementation adds a variety of statistical significance tests at each decision point that allow the user to tailor the algorithm to a particular objective. It also utilizes data structures appropriate for the sparse data sets customary in contemporary data mining problems. Two examples that illustrate different approaches to assessing model quality tests are given in this paper. <s> BIB003
(a reduced set of [7] inputs and the output) Step #2: Search through the lattice of multicomponent-predicting models In the first step, the 24 inputs were reduced to 12 and then to 7. This is "feature selection." The subset of 7 inputs were selected by examining the uncertainty reductions achieved in single-predicting-component models. For 7 inputs, the best model is BHLNORUX. The uncertainty reduction of X from the 7 inputs in this model is 81.7%, but this reduction is not statistically significant (α = 1.0). The 7 input model was chosen even though it was not significant because of the expectation that by decomposition the model could be simplified without major loss of predictive power. This was indeed possible. Step two yielded an 11-predicting-component model, reducing the uncertainty of the output by 65.4%, a reduction which is statistically significant (α = 0.03). The model utilizes all 7 inputs, but in components each having 4 or 5 inputs. This decomposition preserved 80% (65.4/81.7) of the predictive information in the single-predicting-component model (α = 1.0) obtained in the first step, BHLNORUX. This second step, unlike the first step, does require operations on the full state space, which here has cardinality 32,400. Only 7 rather than a greater number of inputs were used because of the combinatorial limits of the program, which examined all single-predicting-component (SPC) models by brute force. (The program generated all models with one, two, three, etc. predicting inputs.) However, in more recent versions of the program, this brute force approach has been replaced by a breadth-first fixed width search, which allows the rapid examination of an arbitrary number of inputs. Also a related RA method exists, known as "Extended Dependency Analysis" (EDA), which allows heuristic search in single-predicting component models with 10's or 100's of variables BIB001 BIB003 . EDA merges the inputs of several good components to produce a singlecomponent model, without evaluating all models with this number of total inputs. Recently, in an RA study looking only at SPC models, 150 variables were reduced to 46 for subsequent use by neural net modeling BIB002 ). To repeat a point made earlier: these calculations, involving SPC models, do not require operations on a complete state space and scale with the size of the data, which for the subset of OPUS data we used is quite modest, namely N=2100.