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

Keyphrases

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

INIS

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

Computer Science

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