Pareto-Optimal Data Compression for Binary Classification Tasks

Binary Linear Compression for Multi-label Classification

In multi-label classification tasks, labels are commonly related with each other. It has been well recognized that utilizing label relationship is essential to multi-label learning. One way to utilizing label relationship is to map labels to a lower-dimensional space of uncorrelated labels, where the relationship could be encoded in the mapping. Previous linear mapping methods commonly result i...

Multilayer perceptron architectures for data compression tasks

Different kinds of Multilayer Perceptrons, using a back-propagation learning algonthm, have been used to perform data compression tasks. Depending upon the architecture and the type of problern learned to solve ( classification or auto-association), the networks provide different kinds of dimensionality reduction preserving different properties of the data space. Some experiments show that usmg...

Constructing feed-forward neural networks for binary classification tasks

Efficient Adaptive Learning for Classification Tasks with Binary Units

This article presents a new incremental learning algorithm for classification tasks, called NetLines, which is well adapted for both binary and real-valued input patterns. It generates small, compact feedforward neural networks with one hidden layer of binary units and binary output units. A convergence theorem ensures that solutions with a finite number of hidden units exist for both binary an...

Optimal Feature Selection for Data Classification and Clustering: Techniques and Guidelines

In this paper, principles and existing feature selection methods for classifying and clustering data be introduced. To that end, categorizing frameworks for finding selected subsets, namely, search-based and non-search based procedures as well as evaluation criteria and data mining tasks are discussed. In the following, a platform is developed as an intermediate step toward developing an intell...