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|AbstractTimeSeries||An abstract instance filter that assumes instances form time-series data and performs some merging of attribute values in the current instance with attribute attribute values of some previous (or future) instance.|
|Add||An instance filter that adds a new attribute to the dataset.|
|AddCluster||A filter that adds a new nominal attribute representing the cluster assigned to each instance by the specified clustering algorithm.|
|AddExpression||An instance filter that creates a new attribute by applying a mathematical expression to existing attributes.|
|AddID||An instance filter that adds an ID attribute to the dataset.|
|AddNoise||An instance filter that changes a percentage of a given attributes values.|
|AddValues||Adds the labels from the given list to an attribute if they are missing.|
|Center||Centers all numeric attributes in the given dataset to have zero mean (apart from the class attribute, if set).|
|ChangeDateFormat||Changes the date format used by a date attribute.|
|ClassAssigner||Filter that can set and unset the class index.|
|ClusterMembership||A filter that uses a density-based clusterer to generate cluster membership values; filtered instances are composed of these values plus the class attribute (if set in the input data).|
|Copy||An instance filter that copies a range of attributes in the dataset.|
|Discretize||An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.|
|FirstOrder||This instance filter takes a range of N numeric attributes and replaces them with N-1 numeric attributes, the values of which are the difference between consecutive attribute values from the original instance.|
|InterquartileRange||A filter for detecting outliers and extreme values based on interquartile ranges.|
|KernelFilter||Converts the given set of predictor variables into a kernel matrix.|
|MakeIndicator||A filter that creates a new dataset with a boolean attribute replacing a nominal attribute.|
|MathExpression||Modify numeric attributes according to a given expression Valid options are:|
|MergeTwoValues||Merges two values of a nominal attribute into one value.|
|MultiInstanceToPropositional||Converts the multi-instance dataset into single instance dataset so that the Nominalize, Standardize and other type of filters or transformation can be applied to these data for the further preprocessing.
Note: the first attribute of the converted dataset is a nominal attribute and refers to the bagId.
|NominalToBinary||Converts all nominal attributes into binary numeric attributes.|
|NominalToString||Converts a nominal attribute (i.e.|
|Normalize||Normalizes all numeric values in the given dataset (apart from the class attribute, if set).|
|NumericCleaner||A filter that 'cleanses' the numeric data from values that are too small, too big or very close to a certain value (e.g., 0) and sets these values to a pre-defined default.|
|NumericToBinary||Converts all numeric attributes into binary attributes (apart from the class attribute, if set): if the value of the numeric attribute is exactly zero, the value of the new attribute will be zero.|
|NumericToNominal||A filter for turning numeric attributes into nominal ones.|
|NumericTransform||Transforms numeric attributes using a given transformation method.|
|Obfuscate||A simple instance filter that renames the relation, all attribute names and all nominal (and string) attribute values.|
|PartitionedMultiFilter||A filter that applies filters on subsets of attributes and assembles the output into a new dataset.|
|PKIDiscretize||Discretizes numeric attributes using equal frequency binning, where the number of bins is equal to the square root of the number of non-missing values.
For more information, see:
Ying Yang, Geoffrey I.
|PotentialClassIgnorer||This filter should be extended by other unsupervised attribute filters to allow processing of the class attribute if that's required.|
|PrincipalComponents||Performs a principal components analysis and transformation of the data.
Dimensionality reduction is accomplished by choosing enough eigenvectors to account for some percentage of the variance in the original data -- default 0.95 (95%).
Based on code of the attribute selection scheme 'PrincipalComponents' by Mark Hall and Gabi Schmidberger.
|PropositionalToMultiInstance||Converts the propositional instance dataset into multi-instance dataset (with relational attribute).|
|RandomProjection||Reduces the dimensionality of the data by projecting it onto a lower dimensional subspace using a random matrix with columns of unit length (i.e.|
|RandomSubset||Chooses a random subset of attributes, either an absolute number or a percentage.|
|RELAGGS||A propositionalization filter inspired by the RELAGGS algorithm.
It processes all relational attributes that fall into the user defined range (all others are skipped, i.e., not added to the output).
|Remove||A filter that removes a range of attributes from the dataset.|
|RemoveType||Removes attributes of a given type.|
|RemoveUseless||This filter removes attributes that do not vary at all or that vary too much.|
|Reorder||A filter that generates output with a new order of the attributes.|
|ReplaceMissingValues||Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.|
|Standardize||Standardizes all numeric attributes in the given dataset to have zero mean and unit variance (apart from the class attribute, if set).|
|StringToNominal||Converts a string attribute (i.e.|
|StringToWordVector||Converts String attributes into a set of attributes representing word occurrence (depending on the tokenizer) information from the text contained in the strings.|
|SwapValues||Swaps two values of a nominal attribute.|
|TimeSeriesDelta||An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the difference between the current value and the equivalent attribute attribute value of some previous (or future) instance.|
|TimeSeriesTranslate||An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the equivalent attribute values of some previous (or future) instance.|
|Wavelet||A filter for wavelet transformation.
For more information see:
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