Data processing apparatus and method for automatically generating a classification component

Abstract

Data processing apparatus operative to generate a classification component is disclosed. The data processing apparatus is configured to provide a template classifier bank comprising a plurality of classifier modules, each classifier module operative to receive training data comprising data elements having one of two or more known class affiliations and to output a class affiliation estimate value for each input data element. The data processing apparatus is further configured to derive a combination of the class affiliation estimate values providing a highest correlation to the two or more known class affiliations, and to generate a classification component formed of a resultant classifier bank comprising a combination of the plurality of classifier modules corresponding to the combination of estimate values providing the highest correlation.

1 - 62 . (canceled) 
     
     
         63 . A data processing apparatus operative to generate a classification component, said data processing apparatus configured to:
 (a) provide a template classifier bank comprising a plurality of classifier modules, each classifier module operative to receive training data comprising data elements having one of two or more known class affiliations and to output a class affiliation estimate value for each input data element;   (b) derive a combination of said class affiliation estimate values providing a highest correlation to said two or more known class affiliations, said highest correlation being represented by a highest correlation value derived from a mathematical combination of said class affiliation estimate values; and   (c) generate a classification component formed of a resultant classifier bank comprising a combination of said plurality of classifier modules corresponding to said combination of estimate values providing said highest correlation value;   (d) input said class affiliation estimate values whose combination provided said respective highest correlation to each of said plurality of classifier modules of said template classifier bank to obtain further class affiliation estimate values and to derive a combination thereof which provides a further highest correlation to said two or more known class affiliations, said further highest correlation being represented by a further highest correlation value;   (e) generate a multi-stage classification component formed of said resultant classifier bank and a further resultant classifier bank comprising a combination of said plurality of classifier modules corresponding to said combination of further class affiliation estimate values providing said further highest correlation value for said further highest correlation being greater than said highest correlation value;   (f) arrange said multi-stage classification component such that class affiliation estimate values output from classifier modules of said resultant classifier bank are input to classifier modules of said further resultant classifier bank;   wherein said resultant classifier bank comprises a sub-set of said plurality of classifier modules forming said template classifier bank and said further resultant classifier bank comprises a sub-set of said plurality of classifier modules forming said resultant classifier bank; and   further wherein if said further highest correlation value is greater than said highest correlation value, said further resultant classifier bank comprises a final classifier bank, but if said further highest correlation value is less than said highest correlation value, said apparatus is further operative to provide said further resultant classifier bank as said template classifier bank and to repeat operations (a) to (f).   
     
     
         64 . The data processing apparatus according to  claim 63 , configured to deselect classifier modules from said template classifier bank to generate said resultant classifier bank. 
     
     
         65 . The data processing apparatus according to  claim 63 , configured to apply a zero weighting to outputs of said template classifier bank not corresponding to said combination of estimate values providing said highest correlation. 
     
     
         66 . The data processing apparatus according to  claim 63 , further configured such that said combination of class affiliation estimate values provides respective highest correlation values to said two or more known class affiliations, and to generate said resultant classifier bank comprising a combination of said plurality of classifier modules corresponding to said combination of class affiliation estimate values providing said respective highest correlation values. 
     
     
         67 . The data processing apparatus according to  claim 63 , further configured such that said resultant classifier bank comprises a combination of said plurality of classifier modules corresponding to said combination of class affiliation estimate values providing said highest correlation value. 
     
     
         68 . The data processing apparatus according to  claim 63 , wherein said mathematical combination comprises a mean value of said class affiliation estimate values. 
     
     
         69 . The data processing apparatus according to  claim 63 , further configured to analyse said class affiliation estimate values using regression analysis in order to derive said combination of class affiliation estimate values. 
     
     
         70 . The data processing apparatus according to  claim 63 , configured to deselect classifier modules from said template classifier bank not corresponding to said combination of estimate values providing said highest correlation to generate said further resultant classifier bank. 
     
     
         71 . The data processing apparatus according to  claim 63 , configured to apply a zero weighting to outputs of said template classifier bank not corresponding to said combination of estimate values providing said highest correlation to generate said further resultant classifier bank. 
     
     
         72 . The data processing apparatus according to  claim 63 , further configured such that said combination of further class affiliation estimate values provides respective highest correlation values to said two or more known class affiliations, and to generate said further resultant classifier bank comprising a combination of said plurality of classifier modules corresponding to said combination of further class affiliation estimate values providing said respective highest correlation values. 
     
     
         73 . The data processing apparatus according to  claim 63 , further configured such that said highest correlation is represented by a highest correlation value derived from a mathematical combination of said further class affiliation estimate values, and said further resultant classifier bank comprises a combination of said plurality of classifier modules corresponding to said combination of further class affiliation estimate values providing said highest correlation value. 
     
     
         74 . The data processing apparatus according to  claim 73 , wherein said mathematical combination comprises a mean value of said further class affiliation estimate values. 
     
     
         75 . The data processing apparatus according to  claim 63 , further configured to analyse said further class affiliation estimate values using regression analysis in order to derive said combination of further class affiliation estimate values. 
     
     
         76 . The data processing apparatus according to  claim 63 , wherein said data elements are representative of source data of said known class affiliation. 
     
     
         77 . The data processing apparatus according to  claim 63 , further configured to normalize said training data. 
     
     
         78 . A method of operating data processing apparatus to generate a classification component, comprising:
 (a) providing a template classifier bank comprising a plurality of classifier modules, each classifier module operative to receive training data comprising data elements having one of two or more known class affiliations and to output a class affiliation estimate value for each input data element;   (b) deriving a combination of said class affiliation estimate values providing a highest correlation to said two or more known class affiliations, said highest correlation being represented by a highest correlation value derived from a mathematical combination of said class affiliation estimate values;   (c) generating a classification component formed of a resultant classifier bank comprising a combination of said plurality of classifier modules corresponding to said combination of estimate values providing said highest correlation;   (d) inputting said class affiliation estimate values whose combination provided said respective highest correlation to each of said plurality of classifier modules of said template classifier bank to obtain further class affiliation estimate values, deriving a combination thereof which provides a further highest correlation to said two or more known class affiliations, said further highest correlation being represented by a further highest correlation value;   (e) generating a multi-stage classification component formed of said resultant classifier bank and a further resultant classifier bank comprising a combination of said plurality of classifier modules corresponding to said combination of further class affiliation estimate values providing said further highest correlation value for said further highest correlation being greater than said highest correlation value;   (f) arranging said multi-stage classification component such that class affiliation estimate values output from classifier modules of said resultant classifier bank are input to classifier modules of said further resultant classifier bank;   wherein said resultant classifier bank comprises a sub-set of said plurality of classifier modules forming said template classifier bank and said further resultant classifier bank comprises a sub-set of said plurality of classifier modules forming said resultant classifier bank; and   further wherein if said further highest correlation value is greater than said highest correlation value, said further resultant classifier bank comprises a final classifier bank, but if said further highest correlation value is less than said highest correlation value, said method further comprising providing said further resultant classifier bank as said template classifier bank and repeating (a) to (f).   
     
     
         79 . The method according to  claim 78 , further comprising deselecting classifier modules from said template classifier bank to generate said resultant classifier bank. 
     
     
         80 . The method according to  claim 78 , further comprising applying a zero weighting to outputs of said template classifier bank not corresponding to said combination of estimate values providing said highest correlation. 
     
     
         81 . The method according to  claim 78 , wherein said combination of class affiliation estimate values provides respective highest correlation values to said two or more known class affiliations, and further comprising generating said resultant classifier bank comprising a combination of said plurality of classifier modules corresponding to said combination of class affiliation estimate values providing said respective highest correlation values. 
     
     
         82 . The method according to  claim 78 , wherein said resultant classifier bank comprises a combination of said plurality of classifier modules corresponding to said combination of class affiliation estimate values providing said highest correlation value. 
     
     
         83 . The method according to  claim 78 , wherein said mathematical combination comprises a mean value of said class affiliation estimate values. 
     
     
         84 . The method according to  claim 78 , further configured to analyse said class affiliation estimate values using regression analysis in order to derive said combination of class affiliation estimate values. 
     
     
         85 . The method according to  claim 78 , further comprising deselecting classifier modules from said template classifier bank not corresponding to said combination of estimate values providing said highest correlation to generate said further resultant classifier bank. 
     
     
         86 . The method according to  claim 78 , further comprising applying a zero weighting to outputs of said template classifier bank not corresponding to said combination of estimate values providing said highest correlation to generate said further resultant classifier bank. 
     
     
         87 . The method according to  claim 78 , wherein said combination of further class affiliation estimate values provides respective highest correlation values to said two or more known class affiliations, and to generate said further resultant classifier bank comprising a combination of said plurality of classifier modules corresponding to said combination of further class affiliation estimate values providing said respective highest correlation values. 
     
     
         88 . The method according to  claim 78 , wherein said highest correlation is represented by a highest correlation value derived from a mathematical combination of said further class affiliation estimate values, and said further resultant classifier bank comprises a combination of said plurality of classifier modules corresponding to said combination of further class affiliation estimate values providing said highest correlation value. 
     
     
         89 . The method according to  claim 88 , wherein said mathematical combination comprises a mean value of said further class affiliation estimate values. 
     
     
         90 . The method according to  claim 78 , further configured to analyse said further class affiliation estimate values using regression analysis in order to derive said combination of further class affiliation estimate values. 
     
     
         91 . The method according to  claim 78 , wherein said data elements are representative of source data of said known class affiliation. 
     
     
         92 . The method according to  claim 78 , further configured to normalise said training data.