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We have Z-score, IQR etc to identify outliers in data. This could be used to eliminate outliers even in labels. For e.g. if the target variable is a housing price, we could use inter-quartile ranges to isolate outliers.

I am looking at an algorithm or a programming practice to isolate outliers in target variable data for classification exercises. I feel this is pertinent because training data could have samples with incorrect classifications. Maybe the source of error was manual oversight or maybe a freak sequence of events which normally could not have occurred. So eliminating these rows should ideally enable the algorithm to perform better classifications. Any help is appreciated

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