Similarity Matching of Temporal Event-Interval Sequences S . MO H - - PowerPoint PPT Presentation

Similarity Matching of Temporal Event-Interval Sequences

S . MO H A MMA D MI R B A G H E R I A N D H O WA RD J . H A MI LTO N U N I V E R S I T Y O F R E G I N A , R E G I N A , C A N A D A

Outline

1. Introduction 2. Problem Statement 3. Similarity Matching 4. Experiments 5. Conclusion

2

Introduction

• Interval-based event sequences (e-sequence )
• Sequences of events persist over intervals of time of varying lengths
• Exist in many application domains such as medicine, sensor networks,

and sign languages

• E-sequence dataset
• Contains longitudinal data : instances are described by a series of

event intervals

• No features with a single value
• Not organized appropriately for standard machine learning algorithms

3

Problem Statement

• Event interval:
• A triple e = (l, b, f) with event label, beginning and finishing time, e.g.,

(A ,4 ,8)

• E-sequence:
• A list of m event intervals placed in ascending order based on their

beginning times, e.g., = <(A,4,8),(B,6,12),(C,14,18),(D,20,22) >

• E-sequence dataset:
• Set of n e-sequences {,…, } where each e-sequence is

associated with an unique identifier .

4

Problem Statement

An example

• f

e-sequence dataset with 4 e-sequences (e.g., 4 patients ) and 6 event labels (e.g., type of diseases)

5

Problem Statement

• E-sequence sliced time: {4,5,10,12,14,16,18,20,22}
• Coincidence:
• (5,10) = {C,D,E}
• Coincidence label sequence (L-sequence):
• Ordered list of coincidences excluding gaps e.g.,

= < {E},{C,D,E},{C,E},{E},{B},{B,F},{B} >

6

Problem Statement

• Problem:
• Similarity searching and matching of full-length e-sequences
• Contributions:
• We propose and evaluate three novel approaches
• We intuitively view the similarity between two e-sequences and in

terms of:

• Presence of event intervals with the same event labels
• Order of occurrences of these event intervals
• Duration of the event intervals
• Temporal relations among these event intervals

7

Similarity Matching

• 1. Matching Using Relative Frequency
• Relative Frequency:

Duration event interval E in : d(E) = 14-4=10 Duration e-sequence : d() = 22-4= 18

• Function

maps an e-sequence to a vector of

the relative frequencies of event labels

• Distance between the relative frequency vectors of

e-sequences and

8

# event labels event labels

Similarity Matching

• 2. Matching Using Position Code
• Position Code:
• Function

maps an e-sequence to a vector

• f the position codes of event labels
• Distance between the position code vectors of

e-sequences and

9

Coincidence L-sequence

Similarity Matching

• 3. Matching Using Multiple Kernel Learning
• Distance between two e-sequences and

based on Multiple Kernel Learning

10

number of kernels weight of functions (kernels) functions: e.g., {ERF, EPC}

Experiments

• Eight real-world datasets
• Method of evaluation:
• Perform 1-NN classification

prediction for every e-sequence in datasets and

• Recording the fraction of correct

predictions (accuracy)

• Three existing competitors:
• DTW-based method
• Artemis
• IBSM

11

Experiments

• Results:
• EMKL outperforms the Artemis

and DTW-based methods on all datasets

• EMKL vs IBSM:
• Outperforms on four datasets,
• Ties on two datasets
• Loses on two datasets

12

Conclusion

• We propose three distance functions to match the full-length of

event interval sequences.

• 1. The ERF function measures the distance of e-sequences based on

the relative frequency of the event intervals.

• 2. The EPC function matches e-sequences based on the position codes
• f the event intervals.
• 3. The EMKL function combines the ERF and EPC functions.
• Experiments show that EMKL method is an effective approach to

the task of matching of full-length e-sequences and it is a better choice compared to the state-of-the-art methods.

13