Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Data reduction is a common pre-processing step for k-nearest neighbor#R##N#   classification (kNN). The existing prototype selection methods implement#R##N#   different criteria for selecting relevant points to use in classification,#R##N#   which constitutes a selection bias. This study examines the nature of the#R##N#   instance selection bias in intrinsically high-dimensional data. In#R##N#   high-dimensional feature spaces, hubs are known to emerge as centers of#R##N#   influence in kNN classification. These points dominate most kNN sets and are#R##N#   often detrimental to classification performance. Our experiments reveal that#R##N#   different instance selection strategies bias the predictions of the behavior#R##N#   of hub-points in high-dimensional data in different ways.  propose to#R##N#   introduce an intermediate un-biasing step when training the neighbor#R##N#   occurrence models and  demonstrate promising improvements in various#R##N#   hubness-aware classification methods, on a wide selection of high-dimensional#R##N#   synthetic and real-world datasets.
['Nenad Tomašev', 'Krisztian Buza', 'Dunja Mladenic']
I am writing a research paper on a idea Correcting the Hub Occurrence Prediction Bias in Many Dimensions
[{"from": "human", "value": "I am writing a research paper on a idea Correcting the Hub Occurrence Prediction Bias in Many Dimensions"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Data reduction is a common pre-processing step for k-nearest neighbor#R##N#   classification (kNN). The existing prototype selection methods implement#R##N#   different criteria for selecting relevant points to use in classification,#R##N#   which constitutes a selection bias. This study examines the nature of the#R##N#   instance selection bias in intrinsically high-dimensional data. In#R##N#   high-dimensional feature spaces, hubs are known to emerge as centers of#R##N#   influence in kNN classification. These points dominate most kNN sets and are#R##N#   often detrimental to classification performance. Our experiments reveal that#R##N#   different instance selection strategies bias the predictions of the behavior#R##N#   of hub-points in high-dimensional data in different ways.  propose to#R##N#   introduce an intermediate un-biasing step when training the neighbor#R##N#   occurrence models and  demonstrate promising improvements in various#R##N#   hubness-aware classification methods, on a wide selection of high-dimensional#R##N#   synthetic and real-world datasets."}]