C4.5: programs for machine learning
C4.5: programs for machine learning
Prediction of generalization ability in learning machines
Prediction of generalization ability in learning machines
Robust Classification for Imprecise Environments
Machine Learning
Gravity based spatial clustering
Proceedings of the 10th ACM international symposium on Advances in geographic information systems
Learning from Examples with Information Theoretic Criteria
Journal of VLSI Signal Processing Systems
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
A Shrinking-Based Dimension Reduction Approach for Multi-Dimensional Data Analysis
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Modeling Theory in Science Education (Science & Technology Education Library)
Modeling Theory in Science Education (Science & Technology Education Library)
Fuzzy integral-based perceptron for two-class pattern classification problems
Information Sciences: an International Journal
A new approach to classification based on association rule mining
Decision Support Systems
Customized classification learning based on query projections
Information Sciences: an International Journal
A self-adaptive migration model genetic algorithm for data mining applications
Information Sciences: an International Journal
A shrinking-based approach for multi-dimensional data analysis
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A Multi-modal Immune Optimization Algorithm for IIR Filter Design
ICICTA '08 Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation - Volume 02
Data gravitation based classification
Information Sciences: an International Journal
GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
Parallel physics-inspired waterflow particle mechanics algorithm for load rebalancing
Computer Networks: The International Journal of Computer and Telecommunications Networking
Electrostatic field framework for supervised and semi-supervised learning from incomplete data
Natural Computing: an international journal
Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
IEEE Transactions on Neural Networks
Hi-index | 0.07 |
Classification is one of the key issues in the fields of decision sciences and knowledge discovery. In this paper, we present a new classification method based on gravitational potential energy between particles. The basic principle of gravitation based classification (GBC) algorithm is to find the equilibrium condition of the classifier, which is modeled as a classifier line between two groups of fixed particles. In the proposed approach, the input data is a collection of masses, which interact with each other based on Newton's universal law of gravitation and the laws of motion. We present a convex formulation for this problem that always converges to a global optimum solution. The proposed method has been compared with some well-known classification approaches, and the results confirm the high performance of the proposed method.