NDLI: Random Forest Based Feature Induction

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2015 IEEE International Conference on Data Mining

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2011 IEEE 11th International Conference on Data Mining

Table of Contents

Message from the Conference General Chairs

Message from the Program Co-Chairs

Steering Committee

Program Committee

List of Reviewers

Algorithms for Mining the Evolution of Conserved Relational States in Dynamic Networks

Infrastructure Pattern Discovery in Configuration Management Databases via Large Sparse Graph Mining

Role-Behavior Analysis from Trajectory Data by Cross-Domain Learning

Semi-supervised Feature Importance Evaluation with Ensemble Learning

COMET: A Recipe for Learning and Using Large Ensembles on Massive Data

Overlapping Correlation Clustering

Learning with Minimum Supervision: A General Framework for Transductive Transfer Learning

Confidence in Predictions from Random Tree Ensembles

Mining Heavy Subgraphs in Time-Evolving Networks

Multi-Class L2,1-Norm Support Vector Machine

SolarMap: Multifaceted Visual Analytics for Topic Exploration

Efficiently Mining Unordered Trees

CEMiner -- An Efficient Algorithm for Mining Closed Patterns from Time Interval-Based Data

LinkBoost: A Novel Cost-Sensitive Boosting Framework for Community-Level Network Link Prediction

Learning Dirichlet Processes from Partially Observed Groups

Exploiting False Discoveries -- Statistical Validation of Patterns and Quality Measures in Subgroup Discovery

An Efficient Greedy Method for Unsupervised Feature Selection

Structured Feature Selection and Task Relationship Inference for Multi-task Learning

A Taxi Driving Fraud Detection System

Isograph: Neighbourhood Graph Construction Based on Geodesic Distance for Semi-supervised Learning

D-cores: Measuring Collaboration of Directed Graphs Based on Degeneracy

SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model

Cross Domain Random Walk for Query Intent Pattern Mining from Search Engine Log

Flexible Fault Tolerant Subspace Clustering for Data with Missing Values

Heuristic Updatable Weighted Random Subspaces for Non-stationary Environments

Learning Tags from Unsegmented Videos of Multiple Human Actions

Generating Breakpoint-based Timeline Overview for News Topic Retrospection

A Robust Clustering Algorithm Based on Aggregated Heat Kernel Mapping

Patent Maintenance Recommendation with Patent Information Network Model

S-preconditioner for Multi-fold Data Reduction with Guaranteed User-Controlled Accuracy

Beyond 'Caveman Communities': Hubs and Spokes for Graph Compression and Mining

Improving Product Classification Using Images

Learning Markov Logic Networks via Functional Gradient Boosting

Signature Pattern Covering via Local Greedy Algorithm and Pattern Shrink

TWITOBI: A Recommendation System for Twitter Using Probabilistic Modeling

Maximum Entropy Modelling for Assessing Results on Real-Valued Data

Local Models for Expectation-Driven Subgroup Discovery

Context-Aware Multi-instance Learning Based on Hierarchical Sparse Representation

The Joint Inference of Topic Diffusion and Evolution in Social Communities

Towards Optimal Discriminating Order for Multiclass Classification

A Hypergraph-based Method for Discovering Semantically Associated Itemsets

Personalized Travel Package Recommendation

Tag Clustering and Refinement on Semantic Unity Graph

Minimizing Seed Set for Viral Marketing

Privacy Risk in Graph Stream Publishing for Social Network Data

Boolean Tensor Factorizations

Sparse Domain Adaptation in Projection Spaces Based on Good Similarity Functions

Incremental Elliptical Boundary Estimation for Anomaly Detection in Wireless Sensor Networks

Learning Classification with Auxiliary Probabilistic Information

Word Cloud Model for Text Categorization

SLIM: Sparse Linear Methods for Top-N Recommender Systems

Novel Recommendation Based on Personal Popularity Tendency

An Analysis of Performance Measures for Binary Classifiers

Detection of Cross-Channel Anomalies from Multiple Data Channels

Threshold Conditions for Arbitrary Cascade Models on Arbitrary Networks

Time Series Epenthesis: Clustering Time Series Streams Requires Ignoring Some Data

Mining Historical Documents for Near-Duplicate Figures

Analysis of Textual Variation by Latent Tree Structures

Healing Sample Selection Bias by Source Classifier Selection

An In-depth Study of Stochastic Kronecker Graphs

Learning Spectral Embedding for Semi-supervised Clustering

Detection of Arbitrarily Oriented Synchronized Clusters in High-Dimensional Data

A Generalized Fast Subset Sums Framework for Bayesian Event Detection

Learning to Rank for Query-Focused Multi-document Summarization

Simple Multiple Noisy Label Utilization Strategies

Partitionable Kernels for Mapping Kernels

Mining Dominant Patterns in the Sky

Ranking Web-Based Partial Orders by Significance Using a Markov Reference Model

Interesting Multi-relational Patterns

On Generating All Optimal Monotone Classifications

Recursive Multi-step Time Series Forecasting by Perturbing Data

Finding Robust Itemsets under Subsampling

Density Estimation Based on Mass

Diverse Dimension Decomposition of an Itemset Space

Conditional Anomaly Detection with Soft Harmonic Functions

Random Forest Based Feature Induction

Class Imbalance, Redux

Combining Feature Context and Spatial Context for Image Pattern Discovery

Nonnegative Matrix Tri-factorization Based High-Order Co-clustering and Its Fast Implementation

Detecting Community Kernels in Large Social Networks

ADANA: Active Name Disambiguation

Document Clustering via Matrix Representation

Using Bayesian Network Learning Algorithm to Discover Causal Relations in Multivariate Time Series

Efficient Mining of a Concise and Lossless Representation of High Utility Itemsets

Understanding Propagation Error and Its Effect on Collective Classification

Direct Robust Matrix Factorizatoin for Anomaly Detection

A New Markov Model for Clustering Categorical Sequences

BibClus: A Clustering Algorithm of Bibliographic Networks by Message Passing on Center Linkage Structure

Multi-instance Metric Learning

Multi-task Learning with Task Relations

Secure Clustering in Private Networks

LPTA: A Probabilistic Model for Latent Periodic Topic Analysis

Causal Associative Classification

Multi-task Learning for Bayesian Matrix Factorization

Enabling Fast Lazy Learning for Data Streams

Clusterability Analysis and Incremental Sampling for Nyström Extension Based Spectral Clustering

Fast and Robust Graph-based Transductive Learning via Minimum Tree Cut

Positive and Unlabeled Learning for Graph Classification

Finding Novel Diagnostic Gene Patterns Based on Interesting Non-redundant Contrast Sequence Rules

Semi-supervised Hierarchical Clustering

Handling Conditional Discrimination

On the Hardness of Graph Anonymization

SPO: Structure Preserving Oversampling for Imbalanced Time Series Classification

Manifold Learning and Missing Data Recovery through Unsupervised Regression

Characterizing Inverse Time Dependency in Multi-class Learning

Supervised Lazy Random Walk for Topic-Focused Multi-document Summarization

Unsupervised Anomaly Intrusion Detection via Localized Bayesian Feature Selection

Identifying Differentially Expressed Genes via Weighted Rank Aggregation

Efficient Mining of Closed Sequential Patterns on Stream Sliding Window

A Spectral Framework for Detecting Inconsistency across Multi-source Object Relationships

Tracking and Connecting Topics via Incremental Hierarchical Dirichlet Processes

Learning Protein Folding Energy Functions

How Does Research Evolve? Pattern Mining for Research Meme Cycles

Twin Gaussian Processes for Binary Classification

Constraint Selection-Based Semi-supervised Feature Selection

Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL

Discovery of Versatile Temporal Subspace Patterns in 3-D Datasets

A Fixed Parameter Tractable Integer Program for Finding the Maximum Order Preserving Submatrix

ASAP: A Self-Adaptive Prediction System for Instant Cloud Resource Demand Provisioning

Learning from Negative Examples in Set-Expansion

Entropy-Based Graph Clustering: Application to Biological and Social Networks

Semi-supervised Discriminant Hashing

Using Frequent Closed Itemsets for Data Dimensionality Reduction

Modeling High-Level Behavior Patterns for Precise Similarity Analysis of Software

Co-clustering for Binary and Categorical Data with Maximum Modularity

Calculating Feature Weights in Naive Bayes with Kullback-Leibler Measure

Scalable Diversified Ranking on Large Graphs

Web Horror Image Recognition Based on Context-Aware Multi-instance Learning

Mixture of Softmax sLDA

Optimizing Performance Measures for Feature Selection

Detecting Recurring and Novel Classes in Concept-Drifting Data Streams

Performances and Characteristics of DIGRank, Ranking in the Incomplete Networks

Cross-Temporal Link Prediction

Helix: Unsupervised Grammar Induction for Structured Activity Recognition

Distance Preserving Graph Simplification

Clustering with Attribute-Level Constraints

A Fast and Flexible Clustering Algorithm Using Binary Discretization

A New Multi-task Learning Method for Personalized Activity Recognition

Identities Anonymization in Dynamic Social Networks

Discovering Emerging Topics in Social Streams via Link Anomaly Detection

Finding Communities in Dynamic Social Networks

Review Graph Based Online Store Review Spammer Detection

Classifying Categorical Data by Rule-Based Neighbors

Tensor Fold-in Algorithms for Social Tagging Prediction

Discovering Thematic Patterns in Videos via Cohesive Sub-graph Mining

Low Rank Metric Learning with Manifold Regularization

A Study of Laplacian Spectra of Graph for Subgraph Queries

Text Clustering via Constrained Nonnegative Matrix Factorization

Publisher's Information

2010 IEEE International Conference on Data Mining

2009 Ninth IEEE International Conference on Data Mining

2008 Eighth IEEE International Conference on Data Mining

Seventh IEEE International Conference on Data Mining (ICDM 2007)

Sixth International Conference on Data Mining (ICDM'06)

Fifth IEEE International Conference on Data Mining (ICDM'05)

Fourth IEEE International Conference on Data Mining (ICDM'04)

Third IEEE International Conference on Data Mining

2002 IEEE International Conference on Data Mining, 2002. Proceedings.

Proceedings 2001 IEEE International Conference on Data Mining

Random Forest Based Feature Induction

Content Provider	IEEE Xplore Digital Library
Author	Vens, C. Costa, F.
Copyright Year	2011
Abstract	We propose a simple yet effective strategy to induce a task dependent feature representation using ensembles of random decision trees. The new feature mapping is efficient in space and time, and provides a metric transformation that is non parametric and not implicit in nature (i.e. not expressed via a kernel matrix), nor limited to the transductive setup. The main advantage of the proposed mapping lies in its flexibility to adapt to several types of learning tasks ranging from regression to multi-label classification, and to deal in a natural way with missing values. Finally, we provide an extensive empirical study of the properties of the learned feature representation over real and artificial datasets.
Starting Page	744
Ending Page	753
File Size	407969
Page Count	10
File Format	PDF
ISBN	9781457720758
ISSN	15504786
DOI	10.1109/ICDM.2011.121
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-11
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vegetation Kernel Decision trees Complexity theory Training Vectors Encoding random forests feature induction
Content Type	Text
Resource Type	Article

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