Neural network negative rule extraction
AssigneeInternational Business Machines Corporation
InventorsFranck Vinh Le, Mudhakar Srivatsa
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Abstract
A first classification is received from a neural network regarding a training dataset sent to the neural network. A modified training dataset with a perturbation of the training dataset is identified, where this modified training dataset causes the neural network to return a second classification. The perturbation is analyzed to identify a negative rule of the neural network.
Claims20
A computer-implemented method comprising:
- receiving a first classification from a neural network regarding a training dataset sent to the neural network, wherein the neural network is configured to identify positive and negative evidence;
- identifying a modified training dataset with a perturbation of the training dataset that causes the neural network to return a second classification that is different from the first classification;
- and
- analyzing the perturbation to identify a negative rule of the neural network, wherein the negative rule relates to an absence of an element in an input of the training dataset increasing a likelihood that the neural network returns the first classification, wherein a presence of the element increases a likelihood that the neural network returns the second classification.
A system comprising:
- a processor;
- and
- a memory in communication with the processor, the memory containing instructions that, when executed by the processor, cause the processor to:
- receive a first classification from a neural network regarding a training dataset sent to the neural network, wherein the neural network is configured to identify positive and negative evidence;
- identify a modified training dataset with a perturbation of the training dataset that causes the neural network to return a second classification that is different from the first classification;
- and
- analyze the perturbation to identify a negative rule of the neural network, wherein the negative rule relates to an absence of an element in an input of the training dataset increasing a likelihood that the neural network returns the first classification, wherein a presence of the element increases a likelihood that the neural network returns the second classification.
A computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to:
- receive a first classification from a neural network regarding a training dataset sent to the neural network, wherein the neural network is configured to identify positive and negative evidence;
- identify a modified training dataset with a perturbation of the training dataset that causes the neural network to return a second classification that is different from the first classification;
- and
- analyze the perturbation to identify a negative rule of the neural network, wherein the negative rule relates to an absence of an element in an input of the training dataset increasing a likelihood that the neural network returns the first classification, wherein a presence of the element increases a likelihood that the neural network returns the second classification.
Dependencies are inferred from the claim text.