Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   learn a dissimilarity measure for categorical data, for eective classication of the data points. Each categorical feature (with values taken from a nite set of symbols) is mapped onto a continuous feature whose values are real numbers. Guided by the classication error based on a nearest neighbor based technique,  repeatedly update the assignment of categorical symbols to real numbers to minimize this error. Intuitively, the algorithm pushes together points with the same class label, while enlarging the distances to points labeled dierently. Our experiments show that 1) the learned dissimilarities improve classication accuracy by using the anities of categorical symbols; 2) they outperform dissimilarities produced by previous data-driven methods; 3) our enhanced nearest neighbor classier (called LD) based on the new space is competitive compared with classiers such
['Jierui Xie', 'Boleslaw K. Szymanski', 'Mohammed J. Zaki']
I am writing a research paper on a idea Learning Dissimilarities for Categorical Symbols
[{"from": "human", "value": "I am writing a research paper on a idea Learning Dissimilarities for Categorical Symbols"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   learn a dissimilarity measure for categorical data, for eective classication of the data points. Each categorical feature (with values taken from a nite set of symbols) is mapped onto a continuous feature whose values are real numbers. Guided by the classication error based on a nearest neighbor based technique,  repeatedly update the assignment of categorical symbols to real numbers to minimize this error. Intuitively, the algorithm pushes together points with the same class label, while enlarging the distances to points labeled dierently. Our experiments show that 1) the learned dissimilarities improve classication accuracy by using the anities of categorical symbols; 2) they outperform dissimilarities produced by previous data-driven methods; 3) our enhanced nearest neighbor classier (called LD) based on the new space is competitive compared with classiers such"}]