A Review on Classification Algorithms in Data Mining
Ling Li · Chongqing Shifan Daxue xuebao. Ziran kexue ban · 2011
In this paper,we analyzed some key problems that must be solved in classification.Then,the idea and characteristic of main kinds of classification algorithms are reviewed.Decision tree algorithm can handle noise data well but is only effective to small datasets.Bayesian has the merits of high accuracy,fast speed,low mistake rate and demerits of low accuracy.Classification based on association rule has advantages of high accuracy but is limited to random access memory.Support vector machine has the merits of high accuracy,low complexity but shows bad time complexity.According to the advantages and disadvantages of the well-known algorithms,some recent proposed classification algorithms which achieve better performance are addressed,such as multi-decision fusion technology,the hybrid classification algorithm based on Bayesian and information gain,and neural network classification algorithm based on rough set and genetic algorithm etc.Finally,research emphasis in the future is discussed.