Earticle

초록

영어

The feature selection approach has been regarded as an effective way to remove redundant and irrelevant features. Thus it increases the efficiency of the learning task and improves the learning performance such as learning time, convergence rate, accuracy, etc. Machine learning approaches such as Neural network, Decision tree, Support Vector Machines are well suited for domains characterized by the presence of large amount of data, noisy patterns, and absence of general theory. The main goal of our research is to propose an efficient feature selection algorithm to achieve a cancer diagnosis system with high accuracies, and good adaptability to clinical dataset. We propose a new feature selection algorithm using Random Forest(RF) to be applicable to the learning algorithm for diagnosing Cancer. The experiments on clinical dataset such as leukemia cancer indicate that our proposed methods obtain higher and more stable classification performance than the baseline methods.

목차

Abstract
 1. Introduction
 2. Random Forest
 3. Proposed Algorithm
 4. Experiments and Results
 5. Conclusion
 References

키워드

저자

• Gyoo-Seok Choi [ Department of Computer Science, Chungwoon University, Korea ] Corresponding Author
• Jong-Jin Park [ Department of Internet Computer, Chungwoon University, Korea ]
• Ha-Nam Nguyen [ Department of Computer Engineering, Hanoi University, Vietnam ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380