Name
Title | ||
|---|---|---|
Maximum relevancy maximum complementary feature selection for multi-sensor activity recognition. |
Abstract | ||
|---|---|---|
•We propose a feature selection algorithm using MRMC.•Show that MRMC provides a good result comparing to the 3 popular algorithms.•The complementary measure improves the performance of the Clamping algorithm.•Evaluate the proposed algorithm on 2 well-defined problems and 5 real life data sets. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.eswa.2014.07.052 | Expert Systems with Applications |
Keywords | Field | DocType |
Feature selection,Neural networks,Mutual information,Activity recognition | Data mining,Data set,Feature selection,Computer science,Redundancy (engineering),Data type,Artificial intelligence,Artificial neural network,Pattern recognition,Feature (computer vision),Minimum redundancy feature selection,Mutual information,Machine learning | Journal |
Volume | Issue | ISSN |
42 | 1 | 0957-4174 |
Citations | PageRank | References |
7 | 0.41 | 24 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saisakul Chernbumroong | 1 | 131 | 5.76 |
| Shuang Cang | 2 | 190 | 16.48 |
| hongnian | 3 | 391 | 46.50 |