One stop shopping: feature selection, classification and prediction in a single step

Abstract

We report on the application of a genetic algorithm (GA) for pattern recognition that uses both supervised and transverse learning to mine spectroscopic and proteomic data. The pattern recognition GA selects features that optimize the separation of the classes in a plot of the two or three largest principal components of the data. For training sets with small amounts of labeled data (i.e. data points tagged with a class label) and large amounts of unlabeled data (i.e. data points that are not tagged with a class label), this approach is preferred, as our results show, information in the unlabeled data is used by the fitness function to guide feature selection. The advantages of incorporating transverse learning into the fitness function of the pattern recognition GA have been evaluated in two recently published studies by our group. In one study, Raman spectroscopy and the pattern recognition GA were used to develop a potential method to discriminate hardwoods, softwoods and tropical woods. In a second study, biopsy material of small round blue cell tumors analyzed by cDNA microarrays was identified as to type (Ewings sarcoma, Burkitt's lymphoma, neuroblastoma and rhabdomyosarcoma) through supervised learning implemented by the pattern recognition GA.

Delineation of disease phenotypes associated with esophageal adenocarcinoma by MALDI-IMS-MS analysis of serum N-linked glycans

“…34 This function employs the Hopkins statistic to assess sample clustering. 35–37 By coupling the Hopkins statistic to PCKaNN, features are selected to optimize clustering in the PC plot using all of the data points (both the training set and the blind samples via the Hopkins statistic) while simultaneously seeking to identify features that create class separation using only the labeled data points (training set samples via PCKaNN).…”

Forensic analysis of automotive paints using a pattern recognition assisted infrared library searching system: Ford (2000–2006)

“…Over time, the pattern recognition GA learns its optimal parameters in a manner similar to a neural network. Further details about the fitness function of the pattern recognition GA used in this study can be found elsewhere [16][17][18][19][20][21].…”

Search prefilters for mid‐infrared absorbance spectra of clear coat automotive paint smears using stacked and linear classifiers

“…The pattern recognition GA identifies a set of spectral features that optimize the separation of the classes in a plot of the two or three largest principal components (PCs) of the data. Because PCs maximize variance, the bulk of the information encoded by the selected features is about differences between the classes in the data set.…”