A Comparative Study of Fuzzy Classification Methods on Breast Cancer Data
Ravi Jain, Ajith Abraham
Abstract
In this paper, we examine the performance of four fuzzy rule generation methods on Wisconsin breast cancer data. The first method generates fuzzy if then rules using the mean and the standard deviation of attribute values. The second approach generates fuzzy if then rules using the histogram of attributes values. The third procedure generates fuzzy if then rules with certainty of each attribute into homogeneous fuzzy sets. In the fourth approach, only overlapping areas are partitioned. The first two approaches generate a single fuzzy if then rule for each class by specifying the membership function of each antecedent fuzzy set using the information about attribute values of training patterns. The other two approaches are based on fuzzy grids with homogeneous fuzzy partitions of each attribute. The performance of each approach is evaluated on breast cancer data sets. Simulation results show that the Modified grid approach has a high classification rate of 99.73 %.
Create a lesson
Related papers
BEAKER: An Expert-Curated Benchmark for Embodied Brains in Self-Driving Chemical Laboratories
Fei Lin, Tengchao Zhang, Ziyang Gong et al.
FrontierPhysics: Evaluating Agents for End-to-End Physics Research
Bingran You, Wenjun Ke, Ria Rosenauer et al.
Dull, Dirty, Dangerous? Working Conditions and the Limits of Occupational AI Exposure
Siyi Yu
Learning to Decode Linear Codes with Generalized Belief Propagation Network
Samira Malek
On Turn-Level Best-of-N Sampling for Scaling LLM Agents
Jalaj Bhandari, Dhawal Gupta, Hongli Zhan et al.
CompressionBench: How can your coding agents compress LLMs?
Xinrui Zhong, Zhifeng Li, Zhiming Wang et al.