Communications of the ACM - Special issue on parallelism
Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
C4.5: programs for machine learning
C4.5: programs for machine learning
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Graph Drawing: Algorithms for the Visualization of Graphs
Graph Drawing: Algorithms for the Visualization of Graphs
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Which Aesthetic has the Greatest Effect on Human Understanding?
GD '97 Proceedings of the 5th International Symposium on Graph Drawing
An Improved Bound on the One-Sided Minimum Crossing Number in Two-Layered Drawings
Discrete & Computational Geometry
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Transformations of symbolic data for continuous data oriented models
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Extension of ICF classifiers to real world data sets
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
GD'05 Proceedings of the 13th international conference on Graph Drawing
Layout effects on sociogram perception
GD'05 Proceedings of the 13th international conference on Graph Drawing
Multiclass visual classifier based on bipartite graph representation of decision tables
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
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In this paper, we consider the two-class classification problem, a significant issue in machine learning. From a given set of positive and negative samples, the problem asks to construct a classifier that predicts the classes of future samples with high accuracy. For this problem, we have studied a new visual classifier in our previous works, which is constructed as follows: We first create several decision tables and extract a bipartite graph structure (called an SE-graph) between the given set of samples and the set of created decision tables.We then draw the bipartite graph as a two-layered drawing by using an edge crossing minimization technique, and the resulting drawing acts as a visual classifier. We first describe our background and philosophy on such a visual classifier, and then consider improving its classification accuracy. We demonstrate the effectiveness of our methodology by computational studies using benchmark data sets, where the new classifier outperforms our older version, and is competitive even with such standard classifiers as C4.5 or LibSVM.