Handling incomplete heterogeneous data using VAEs

Handling Incomplete Categorical Data for Supervised Learning

Classification is an important research topic in knowledge discovery. Most of the researches on classification concern that a complete dataset is given as a training dataset and the test data contain all values of attributes without missing. Unfortunately, incomplete data usually exist in real-world applications. In this paper, we propose new handling schemes of learning classification models f...

Image Reconstruction from Incomplete Data

Image Reconstruction from Incomplete Data

Non-deterministic Distance Semantics for Handling Incomplete and Inconsistent Data

We introduce a modular framework for formalizing reasoning with incomplete and inconsistent information. This framework is composed of non-deterministic semantic structures and distance-based considerations. The combination of these two principles leads to a variety of entailment relations that can be used for reasoning about nondeterministic phenomena and are inconsistency-tolerant. We investi...

Comparison of incomplete data handling techniques for neuro-fuzzy system

Real-life data sets sometimes miss some values. The incomplete data needs specialized algorithms or preprocessing that allows the use of the algorithms for complete data. The paper presents a comparison of various techniques for handling incomplete data in the neuro-fuzzy system ANNBFIS. The crucial procedure in the creation of a fuzzy model for the neuro-fuzzy system is the partition of the in...