An Optimized k-NN Approach for Classification on Imbalanced Datasets with Missing Data

被引：8

作者：

Ozan, Ezgi Can ^{[1
]}

Riabchenko, Ekaterina ^{[1
]}

Kiranyaz, Serkan ^{[2
]}

Gabbouj, Moncef ^{[1
]}

机构：

[1] Tampere Univ Technol, Tampere, Finland

[2] Qatar Univ, Coll Engn, Dept Elect Engn, Doha, Qatar

来源：

ADVANCES IN INTELLIGENT DATA ANALYSIS XV | 2016年 / 9897卷

关键词：

k-NN classifier; Missing data; Imbalanced datasets;

D O I：

10.1007/978-3-319-46349-0_34

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we describe our solution for the machine learning prediction challenge in IDA 2016. For the given problem of 2-class classification on an imbalanced dataset with missing data, we first develop an imputation method based on k-NN to estimate the missing values. Then we define a tailored representation for the given problem as an optimization scheme, which consists of learned distance and voting weights for k-NN classification. The proposed solution performs better in terms of the given challenge metric compared to the traditional classification methods such as SVM, AdaBoost or Random Forests.

引用

页码：387 / 392

页数：6

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