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Missing the Forest for the Trees — Object Technology’s Second Hiatus
2006 Edition, Volume 2, January 1, 2006 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

Object technology was first created in 1967, but it had to wait till the mid 80's for widespread acceptance and adoption. We observe that that was not the only hiatus for the technology. Another major hiatus is currently...

A missing power data filling method based on improved random forest algorithm
2019 Edition, Volume 5, December 1, 2019 - CMP

Missing data filling is a key step in power big data preprocessing, which helps to improve the quality and the utilization of electric power data. Due to the limitations of the traditional methods of filling missing data, an improved random forest...

Fuzzy Decision Forest
2003 Edition, January 1, 2003 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

In the past, we have developed and presented a Fuzzy Decision Tree, more recently followed by an extension called a Fuzzy Decision Forest. The idea behind the forest is not only to represent multiple trees, but also to represent test alternatives at...

Forest Learning from Data and its Universal Coding
Volume PP - IEEE - Institute of Electrical and Electronics Engineers, Inc.

This paper considers structure learning from data with n samples of p variables, assuming that the structure is a forest, using the Chow-Liu algorithm. Specifically, for incomplete data, we construct two model selection algorithms that complete in O(p2) steps: one...

Kinect Depth Recovery via the Cooperative Profit Random Forest Algorithm
2018 Edition, July 1, 2018 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

The depth map captured by Kinect usually contain missing depth data. In this paper, we propose a novel method to recover the missing depth data with the guidance of depth information of each neighborhood pixel. In the proposed framework, a self-taught...

imPhy: Imputing Phylogenetic Trees with Missing Information using Mathematical Programming
Volume PP - IEEE - Institute of Electrical and Electronics Engineers, Inc.

Given a set of organisms, the available corresponding genetic information is often incomplete and most gene trees fail to contain all individuals. This incompleteness causes difficulties in data collection, information extraction, and gene tree inference. Outlying gene...

Structure learning and universal coding when missing values exist
2016 Edition, July 1, 2016 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

This paper considers structure learning from incomplete data with n samples of N variables assuming that the structure is a forest using the Chow-Liu algorithm. We construct two model selection algorithms that complete in O(N2) steps: one obtains a forest with the...

Crumple Trees
2020 Edition, January 1, 2020 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

We introduce the crumple tree, an alternative derivation of the wavl binary search tree that facilitates discovery learning. The crumple tree is distinguished by features that aid teaching and learning: its balance invariant corresponds to the...

Extended Isolation Forest
Volume PP - IEEE - Institute of Electrical and Electronics Engineers, Inc.

We present an extension to the model-free anomaly detection algorithm, Isolation Forest. This extension, named Extended Isolation Forest (EIF), resolves issues with assignment of anomaly score to given data points. We motivate the problem using heat maps for...

Random Forest with Random Projection to Impute Missing Gene Expression Data
2015 Edition, December 1, 2015 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

Measurement error or lack of proper experimental setup often results in invalid or missing data in gene expression studies. Small sample size and cost of re-running the experiment presents a need for an efficient missing data imputation technique. In this paper, we...

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