DPPred: an effective prediction framework with concise discriminative patterns and its biomedical applications

Shang, Jingbo

Permalink

https://hdl.handle.net/2142/97418 Copy

Description

Title

DPPred: an effective prediction framework with concise discriminative patterns and its biomedical applications

Author(s)

Shang, Jingbo

Issue Date

2017-04-24

Director of Research (if dissertation) or Advisor (if thesis)

Han, Jiawei

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Discriminative pattern
• Explanatory
• Concise
• Classification
• Regression
• Biomedical application

Abstract

In the literature, two series of models have been proposed to address prediction problems including classification and regression. Simple models, such as generalized linear models, have ordinary performance but strong interpretability on a set of simple features. The other series, including tree-based models, organize numerical, categorical and high dimensional features into a comprehensive structure with rich interpretable information in the data. In this thesis, we propose a novel discriminative pattern-based prediction framework (DPPred) to accomplish the prediction tasks by taking their advantages of both effectiveness and interpretability. Specifically, DPPred adopts the concise discriminative patterns that are on the prefix paths from the root to leaf nodes in the tree-based models. Moreover, DPPred selects a limited number of the useful discriminative patterns by searching for the most effective pattern combination to fit generalized linear models. To validate the effectiveness of DPPred, we conduct experiments on both classification and regression tasks. Experimental results demonstrate that DPPred provides competitive accuracy with the state-of-the-art as well as the valuable interpretability for developers and experts. In particular, when studying health status for cardiopulmonary patients, DPPred shows the acceptable predicting accuracy (more than 95%) and reveals the importance of demographic features; when studying the amyotrophic lateral sclerosis (ALS) disease, DPPred not only outperforms the baselines by using only 40 concise discriminative patterns out of a potentially exponentially large set of patterns, but also discover novel markers.

Graduation Semester

2017-05

Type of Resource

text

Permalink

http://hdl.handle.net/2142/97418

Copyright and License Information

Copyright 2017 Jingbo Shang

Owning Collections