## 3 Literature Survey
### Frameworks for Data Reduction
After obtaining a solution and measure of performance using some cost function, neighborhood solutions are obtained and tested. Superior solutions are retained, and inferior solutions are either discarded or retained based on a probability calculation. This ability to retain an inferior solution allows the simulated annealing algorithm to \"jump\" out of a local extrema [92]. The intrusion detection case study employed 41 factors, which reduced to 14, 16, and 19 factors when using MLP, SVM, and NBC respectively. A limitation to this approach is that it requires model training at every iteration of the simulated annealing. This may limit the options for which machine learning technique to select; preference should be given to algorithms that quickly converge. Again, for only 41 factors, this is less of an issue. If there are hundreds or thousands, then this approach may be impractical. [PERSON], [PERSON], and [PERSON] (2017) propose an approach for data reduction for coordinate measurement of planar surfaces for the purposes of reducing the number of samples required to adequately validate that a part has been build according to design specifications [93]. The larger context for this approach is manufacturing, but the applicability is narrowly scoped to an inspection station along an assembly line. Thus, this approach could be used in programming firmware for an optical inspection machine so that it can diagnose defective components as efficiently as possible. However, it would not be useful in performing root cause analysis to find the source of the defects or predict future occurrences. [PERSON], [PERSON], and [PERSON] (2016) develop a set of ten features that may be constructed from streaming signal data from semiconductor fabrication equipment. Technological developments allow the collection of inline data at ever increasing sampling rates. This has resulted in two effects, the first being an increase in the amount of data required to store, and the second being the ability to discern features that were previously not discernible [94]. Specifically, high sampling rates allow information to be gleaned from transient periods between steady signal states. This enables the extraction of features from the signal that could not be calculated with lower sampling rates. The approach can be extended to any signal-style continuous data source from which samples are taken, although the implication is that the lower the sampling rate, the less likely that these new features will provide value. These constructed features are applied to case studies of tool and chamber matching and defect level prediction. A reasonable extension might be to apply the approach to machine diagnostic information for active preventive maintenance. From a feature selection or dimensionality perspective, which is of most interest to this review, the ten features are calculated every time the signal transitions from one steady state to another. For relatively static signals, this will result in a manageable feature set; for more dynamic signals or for large time windows, the number of calculated features may become prohibitively large. This could be alleviated by adding an additional layer of features that employ various means to aggregate the values of the ten calculated features over the entire span of time. Continuing on the topic of feature selection, [PERSON], [PERSON], and [PERSON] (2016) propose an algorithm for time series feature extraction, TSFRESH, that not only generates features but also employs a feature importance filter to screen out irrelevant features [95]. This framework, illustrated in Figure 2, begins by extracting up to 794 predefined features from time series data. Subsequently, the vector representing each individual feature is independently tested for significance against the target. This produces a vector of \(p\)-values with the same cardinality as the number of features. Finally, the vector of \(p\)-values is evaluated to decide which features to keep.