Classification of categorical data based on the chi-square dissimilarity and t-sne

Keyphrases

• Chi-square 100%
• Data-centric 100%
• Categorical Data 100%
• T-distributed Stochastic Neighbor Embedding (t-SNE) 100%
• Computation Time 50%
• Separability 50%
• Categorical Variables 50%
• Identification Approach 25%
• New Alternatives 25%
• State-of-the-art Techniques 25%
• Learning Algorithm 25%
• High Computational Cost 25%
• Experimental Configuration 25%
• Categorical Attributes 25%
• Learning Methods 25%
• Dissimilarity Measure 25%
• Application Demand 25%
• UCI Dataset 25%
• Recognition Task 25%
• Mapping Method 25%
• We-map 25%
• Kernel Support Vector Machine 25%
• Reduced Dimensionality 25%
• Categorical Datasets 25%
• Conventional Classifiers 25%

INIS

• data 100%
• classification 100%
• accuracy 100%
• mapping 100%
• learning 66%
• datasets 66%
• performance 66%
• demand 33%
• space 33%
• kernels 33%
• applications 33%
• reduction 33%
• algorithms 33%
• maps 33%
• cost 33%
• configuration 33%

Computer Science

• Computational Time 100%
• Categorical Data 100%
• Categorical Variable 100%
• Support Vector Machine 50%
• k-Nearest Neighbors Algorithm 50%
• Computational Cost 50%
• Learning Algorithm 50%