-
Enhancements of Multi-class Support Vector Machine Construction from Binary Learners using Generalization Performance
We propose several novel methods for enhancing the multi-class SVMs by a...
read it
-
Enhancing Multi-Class Classification of Random Forest using Random Vector Functional Neural Network and Oblique Decision Surfaces
Both neural networks and decision trees are popular machine learning met...
read it
-
DCSVM: Fast Multi-class Classification using Support Vector Machines
We present DCSVM, an efficient algorithm for multi-class classification ...
read it
-
An ensemble of Density based Geometric One-Class Classifier and Genetic Algorithm
One of the most rising issues in recent machine learning research is One...
read it
-
Diversity-Aware Weighted Majority Vote Classifier for Imbalanced Data
In this paper, we propose a diversity-aware ensemble learning based algo...
read it
-
Improving Human Activity Recognition Through Ranking and Re-ranking
We propose two well-motivated ranking-based methods to enhance the perfo...
read it
-
DCMD: Distance-based Classification Using Mixture Distributions on Microbiome Data
Current advances in next generation sequencing techniques have allowed r...
read it
Fast classification using sparse decision DAGs
In this paper we propose an algorithm that builds sparse decision DAGs (directed acyclic graphs) from a list of base classifiers provided by an external learning method such as AdaBoost. The basic idea is to cast the DAG design task as a Markov decision process. Each instance can decide to use or to skip each base classifier, based on the current state of the classifier being built. The result is a sparse decision DAG where the base classifiers are selected in a data-dependent way. The method has a single hyperparameter with a clear semantics of controlling the accuracy/speed trade-off. The algorithm is competitive with state-of-the-art cascade detectors on three object-detection benchmarks, and it clearly outperforms them when there is a small number of base classifiers. Unlike cascades, it is also readily applicable for multi-class classification. Using the multi-class setup, we show on a benchmark web page ranking data set that we can significantly improve the decision speed without harming the performance of the ranker.
READ FULL TEXT
Comments
There are no comments yet.