Feature Selection for Mahalanobis-Taguchi System with Chaotic Quantum Behavior Particle Swarm Optimization

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Taguchi-Particle Swarm Optimization for Numerical Optimization

In this work, a hybrid Taguchi-Particle Swarm Optimization (TPSO) is proposed to solve global numerical optimization problems with continuous and discrete variables. This hybrid algorithm combines the well-known Particle Swarm Optimization Algorithm with the established Taguchi method, which has been an important tool for robust design. This paper presents the improvements obtained despite the ...

متن کامل

Chaotic-based Particle Swarm Optimization with Inertia Weight for Optimization Tasks

Among variety of meta-heuristic population-based search algorithms, particle swarm optimization (PSO) with adaptive inertia weight (AIW) has been considered as a versatile optimization tool, which incorporates the experience of the whole swarm into the movement of particles. Although the exploitation ability of this algorithm is great, it cannot comprehensively explore the search space and may ...

متن کامل

Particle Swarm Optimization based Feature Selection

Feature Selection is a pre-processing step in knowledge discovery from data (KDD) which aims at retrieving relevant data from the database beforehand. It imparts quality to the results of data mining tasks by selecting optimal feature set from larger set of features. Various feature selection techniques have been proposed in past which, unfortunately, suffer from unavoidable problems such as hi...

متن کامل

Optimal Feature Selection of Taguchi Character Recognition in the Mahalanobis-Taguchi System using Bees Algorithm

The Mahalanobis-Taguchi System (MTS) is a data mining method employing Mahalanobis distance (MD) and Taguchi′s Robust Engineering philosophy to explore and exploit data in a multidimensional system. The MD calculation provides a measurement scale to discriminate sample data and gives an approach of measuring the level of severity among them. One unique feature of MTS lies its robustness to asse...

متن کامل

Chaotic Binary Particle Swarm Optimization for Feature Selection using Logistic Map

Feature selection is a useful technique for increasing classification accuracy. The primary objective is to remove irrelevant features in the feature space and identify relevant features. Binary particle swarm optimization (BPSO) has been applied successfully in solving feature selection problem. In this paper, chaotic binary particle swarm optimization (CBPSO) with logistic map for determining...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: DEStech Transactions on Computer Science and Engineering

سال: 2019

ISSN: 2475-8841

DOI: 10.12783/dtcse/cscme2019/32535