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I have a yes/no classification problem, where false positives are worse than false negatives.
Is there a way to implement this fact into neural network especially in MATLAB's Neural Network Toolbox?
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Machine learning systems help to reduce false positive rates in the following ways: Structuring data: False positive remediation involves the analysis of vast amounts of unstructured data, drawn from external sources such as media outlets, social networks, and other public and private records.
The most effective way to reduce both your false positives and negatives is using a high-quality method. This is particularly important in chromatography, though method development work is necessary in other analytical techniques.
A false positive is when a scientist determines something is true when it is actually false (also called a type I error). A false positive is a “false alarm.” A false negative is saying something is false when it is actually true (also called a type II error).
To minimize the number of False Negatives (FN) or False Positives (FP) we can also retrain a model on the same data with slightly different output values more specific to its previous results. This method involves taking a model and training it on a dataset until it optimally reaches a global minimum.
What you need is a cost-sensitive meta-classifier (a meta-classifier works with any arbitrary classifier, be it ANN, SVM, or any other).
This can be done in two ways:
One algorithm that implements the first learning approach is SECOC, which uses error-correcting codes; while an example of the second approach is the MetaCost which uses bagging to improve the probability estimates of the classifier.
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