Semantic Multi-Classifier Systems Identify Predictive Processes in Heart Failure Models across Species

Lausser, Ludwig; Siegle, Lea; Rottbauer, Wolfgang; Frank, Derk; Just, Steffen; Kestler, Hans A.

doi:10.3390/biom8040158

Cited by 1 publication

(2 citation statements)

References 72 publications

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“…The second one is mandatory in the scientific context, where findings on the background of a subject of interest are required for hypothesis generation. An example might be the analysis of molecular profiles in medical applications, where identifying a predictive subset of features (e.g., molecular concentrations) leads to new hypotheses on the causes or mechanisms of a disease or even reveals new drug targets [6][7][8] .…”

Section: Efficient Cross-validation Traversals In Feature Subset Sele...mentioning

confidence: 99%

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Efficient cross-validation traversals in feature subset selection

Lausser

Szekely

Schmid

et al. 2022

Sci Rep

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Sparse and robust classification models have the potential for revealing common predictive patterns that not only allow for categorizing objects into classes but also for generating mechanistic hypotheses. Identifying a small and informative subset of features is their main ingredient. However, the exponential search space of feature subsets and the heuristic nature of selection algorithms limit the coverage of these analyses, even for low-dimensional datasets. We present methods for reducing the computational complexity of feature selection criteria allowing for higher efficiency and coverage of screenings. We achieve this by reducing the preparation costs of high-dimensional subsets $${\mathscr {O}}({n}m^2)$$ O ( n m 2 ) to those of one-dimensional ones $${\mathscr {O}}(m^2)$$ O ( m 2 ) . Our methods are based on a tight interaction between a parallelizable cross-validation traversal strategy and distance-based classification algorithms and can be used with any product distance or kernel. We evaluate the traversal strategy exemplarily in exhaustive feature subset selection experiments (perfect coverage). Its runtime, fitness landscape, and predictive performance are analyzed on publicly available datasets. Even in low-dimensional settings, we achieve approximately a 15-fold increase in exhaustively generating distance matrices for feature combinations bringing a new level of evaluations into reach.

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Section: Efficient Cross-validation Traversals In Feature Subset Sele...mentioning

confidence: 99%

“…For example, more complex or hybrid types of interactions can be found in the context of multi-class classifier systems 9 , 10 . FSS algorithms can also be linked to external domain knowledge to guide the selection process 8 , 36 , 37 .…”

Section: Introductionmentioning

confidence: 99%