27,328 research outputs found
Application of multiobjective genetic programming to the design of robot failure recognition systems
We present an evolutionary approach using multiobjective genetic programming (MOGP) to derive optimal feature extraction preprocessing stages for robot failure detection. This data-driven machine learning method is compared both with conventional (nonevolutionary) classifiers and a set of domain-dependent feature extraction methods. We conclude MOGP is an effective and practical design method for failure recognition systems with enhanced recognition accuracy over conventional classifiers, independent of domain knowledge
An Evaluation of Classification and Outlier Detection Algorithms
This paper evaluates algorithms for classification and outlier detection
accuracies in temporal data. We focus on algorithms that train and classify
rapidly and can be used for systems that need to incorporate new data
regularly. Hence, we compare the accuracy of six fast algorithms using a range
of well-known time-series datasets. The analyses demonstrate that the choice of
algorithm is task and data specific but that we can derive heuristics for
choosing. Gradient Boosting Machines are generally best for classification but
there is no single winner for outlier detection though Gradient Boosting
Machines (again) and Random Forest are better. Hence, we recommend running
evaluations of a number of algorithms using our heuristics
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