Predicting Cardiovascular Risks using Pattern Recognition and Data ...

chosen by the user. The methods for choosing m features are not described in detail in this thesis. Moredetail about this method, and the definition of m features, can be seen in Liu and Motoda (1998).8.2.2. Wrapper MethodThe wrapper method is used as an inductive algorithm to estimate the value of a given feature subset(e.g via cross-validation). This means its goal is to return a subset of features that gives the lowestprediction error. However, according to Dash and Liu (1997) the algorithms exhibit a moderatecomplexity, because the number of executions requires a high computational cost, in particular whenused with exhaustive search strategies. Therefore, this method is not used for the thesis data. Furtherdetails about the wrapper method can be found in Dash and Liu (1997); Liu and Motoda (1998); andTalavera (2005).8.2.3. Relief AlgorithmAlgorithm ReliefInput: For each training instance a vector of attribute values and the class valueOutput: The vector W of estimations of the qualities of attributesset all weights W[A]:=0.0;for i:=1 to m do beginrandomly select an instance R i ;find nearest hit H and nearest miss M;for A:=1 to a doW[A]:= W[A] –diff(A,R i, H)/m +diff(A,R i ,M)/m;end;Figure 8.1: Pseudo code of original relief algorithm (Kira and Rendell, 1992).The Relief algorithm (Kira and Rendell, 1992) is a filter method that estimates the usefulness ofattributes according their values in distinguishing samples that are near each other. The algorithmsearches for two nearest neighbors of each sample in the data domain in the following way: Firstly, itcompares one pattern from the “nearest hit” class with another from the “nearest miss” mis-class. Itupdates the quality estimation according to the “miss” and the “hit” value. This process is repeated126