Modified Multi-Class Miner using Particle of Swarm Optimization for Stream Data Classification
نویسندگان
چکیده
Multi-class miner is well recognized method for stream data classification. For the process of multi-class miner evaluation of new feature during classification is major problem. The problem of feature evaluation decreases the performance of multi-class miner (MCM). For the improvement of multi-class miner particle of swarm optimization technique is used. Particle of swarm optimization controls the dynamic feature evaluation process and decreases the possibility of confusion in selection of class and increase the classification ratio of multi-class miner. Particle of swarm optimization work in two phases one used as dynamic population selection and another are used for optimization process of evolved new feature. For the performance evaluation modified MCM algorithm implemented in MATLAB. For the validation of modified multi-class miner (MMCM) used sample dataset from UCI machine learning repository . Our empirical evaluation shows that better result in compression of multi-class miner and also increases the classification ratio of stream data classification.
منابع مشابه
FUZZY GRAVITATIONAL SEARCH ALGORITHM AN APPROACH FOR DATA MINING
The concept of intelligently controlling the search process of gravitational search algorithm (GSA) is introduced to develop a novel data mining technique. The proposed method is called fuzzy GSA miner (FGSA-miner). At first a fuzzy controller is designed for adaptively controlling the gravitational coefficient and the number of effective objects, as two important parameters which play major ro...
متن کاملNegative Selection Based Data Classification with Flexible Boundaries
One of the most important artificial immune algorithms is negative selection algorithm, which is an anomaly detection and pattern recognition technique; however, recent research has shown the successful application of this algorithm in data classification. Most of the negative selection methods consider deterministic boundaries to distinguish between self and non-self-spaces. In this paper, two...
متن کاملModified CLPSO-based fuzzy classification System: Color Image Segmentation
Fuzzy segmentation is an effective way of segmenting out objects in images containing both random noise and varying illumination. In this paper, a modified method based on the Comprehensive Learning Particle Swarm Optimization (CLPSO) is proposed for pixel classification in HSI color space by selecting a fuzzy classification system with minimum number of fuzzy rules and minimum number of incorr...
متن کاملA Modified Discreet Particle Swarm Optimization for a Multi-level Emergency Supplies Distribution Network
Currently, the research of emergency supplies distribution and decision models mostly focus on deterministic models and exact algorithm. A few of studies have been done on the multi-level distribution network and matheuristic algorithm. In this paper, random processes theory is adopted to establish emergency supplies distribution and decision model for multi-level network. By analyzing the char...
متن کاملS3PSO: Students’ Performance Prediction Based on Particle Swarm Optimization
Nowadays, new methods are required to take advantage of the rich and extensive gold mine of data given the vast content of data particularly created by educational systems. Data mining algorithms have been used in educational systems especially e-learning systems due to the broad usage of these systems. Providing a model to predict final student results in educational course is a reason for usi...
متن کامل