Name
Title | ||
|---|---|---|
ARSkNN: An efficient k-nearest neighbor classification technique using mass based similarity measure. |
Abstract | ||
|---|---|---|
Even though finding out distance is the central core of k-Nearest Neighbor classification techniques, similarity measures are often favored against distance in various realistic scenarios and situation. Most of the similarity measures, which are used to classify an instance, are based on geometric model. Their effectiveness decreases with the increases in the number of dimensions. This paper establishes an efficient technique called ARSkNN for finding out class of any given instance using a measure based on an unique similarity, that does no longer compute distance, for k-NN classification. Our empirical results show that ARSkNN classification technique is better than the previous established k-NN classifiers. The performance of algorithm was verified and validated on various datasets from different domains. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3233/JIFS-169701 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
Keywords | Field | DocType |
Data mining,classification,nearest neighbor,similarity measure | k-nearest neighbors algorithm,Discrete mathematics,Similarity measure,Pattern recognition,Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
35 | 2 | 1064-1246 |
Citations | PageRank | References |
0 | 0.34 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ashish Kumar | 1 | 0 | 1.35 |
| Roheet Bhatnagar | 2 | 0 | 4.39 |
| Sumit Srivastava | 3 | 4 | 3.75 |