Information Theoretic Multi-Target Feature Selection via Output Space Quantization †
Abstract
:1. Introduction
2. Background on Information Theoretic Multi-Target FS
2.1. Deriving Criteria via Maximum Likelihood Maximization Framework
- Joint-JMI
- does not make any assumptions and deals with the joint random variable . This corresponds to the Label Powerset (LP) transformation in the multi-label literature. The main limitation of this method is that is high dimensional. For example, in multi-label problems we have up to distinct labelsets [11], which makes it difficult to estimate MI expressions reliably.
- Single-JMI
- deals with each variable independently of the others. This corresponds to the Binary Relevance (BR) transformation in the multi-label literature. The main limitation of this method is that by making the full independence assumption it ignores possible useful information on how the targets interact with each other.
2.2. Other Information Theoretic Criteria
3. A Novel Framework to Take into Account Target Dependencies
3.1. Transforming Output Space via Quantization to Account for Target Dependencies
- 1st Step—Generate Groups of Target Variables, Using PoT Parameter
- 2nd Step—Low-dimensional Approximations via Quantization, Using NoC Parameter
Algorithm 1 Forward FS with our Group-JMI criterion | |
Input: Dataset , parameters PoT and NoC and the number of features to be selected K. | |
Output: List of top-K features Xθ | |
1: | ▹ Set of candidate features |
2: Set to empty list | ▹ List of selected features |
3: for to m | ▹ Output transformation (where m is the number of target variables) |
4: Use PoT to generate a random subset of targets: | |
5: Derive from the cluster indices: | |
6: end for | |
7: for to K do | |
8: Let maximise: | |
9: | ▹ Our scoring criterion |
10: | ▹ Add feature to the list |
11: | ▹ Remove feature from the candidate set |
12: end for |
3.2. Theoretical Analysis
3.3. Sensitivity Analysis
3.4. A Group-JMI Criterion That Captures Various High-Order Target Interactions
4. Experiments with Multi-Label Data
4.1. Comparing Group-JMI-Rand with Other JMI Criteria
4.2. Comparing Group-JMI-Rand with State-of-the-Art Information Theoretic FS Criteria
5. Experiments with Multivariate Regression Data
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
FS | Feature Selection |
MI | Mutual Information |
CMI | Conditional Mutual Information |
JMI | Joint Mutual Information |
NoC | Number of Clusters |
PoT | Proportion of Targets |
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Name | Application | Examples | Features | Labels | Distinct Labelsets |
---|---|---|---|---|---|
CAL500 | Music | 502 | 68 | 174 | 502 |
emotions | Music | 593 | 72 | 6 | 27 |
enron | Text | 1702 | 1001 | 53 | 752 |
genbase | Biology | 662 | 1186 | 27 | 32 |
image | Images | 2000 | 294 | 5 | 20 |
languagelog | Text | 1460 | 1004 | 75 | 1241 |
medical | Text | 978 | 1449 | 45 | 94 |
scene | Images | 2407 | 294 | 6 | 15 |
yeast | Bioinformatics | 2417 | 103 | 14 | 198 |
(a) Hamming Loss | |||
Single-JMI | Joint-JMI | Group-JMI-Rand | |
(Our Method) | |||
CAL500 | 2.05 | 2.10 | 1.85 |
emotions | 2.33 | 1.85 | 1.82 |
enron | 2.10 | 1.00 | 2.90 |
genbase | 2.11 | 1.71 | 2.17 |
image | 1.90 | 2.73 | 1.38 |
medical | 2.01 | 2.86 | 1.12 |
scene | 1.80 | 1.25 | 2.95 |
yeast | 1.57 | 3.00 | 1.43 |
languagelog | 1.60 | 1.40 | 3.00 |
Total wins | 0 | 4 | 5 |
(b) Ranking Loss | |||
Single-JMI | Joint-JMI | Group-JMI-Rand | |
(Our Method) | |||
CAL500 | 2.20 | 1.93 | 1.88 |
emotions | 1.57 | 2.40 | 2.02 |
enron | 1.75 | 1.30 | 2.95 |
genbase | 2.29 | 1.66 | 2.05 |
image | 1.90 | 2.77 | 1.32 |
medical | 2.11 | 2.79 | 1.10 |
scene | 1.90 | 1.15 | 2.95 |
yeast | 1.52 | 3.00 | 1.48 |
languagelog | 2.62 | 2.38 | 1.00 |
Total wins | 1 | 3 | 5 |
(c) Normalised Coverage | |||
Single-JMI | Joint-JMI | Group-JMI-Rand | |
(Our Method) | |||
CAL500 | 1.75 | 2.35 | 1.90 |
emotions | 1.95 | 2.80 | 1.25 |
enron | 1.82 | 1.25 | 2.92 |
genbase | 2.14 | 1.44 | 2.42 |
image | 2.08 | 2.50 | 1.43 |
languagelog | 2.40 | 2.60 | 1.00 |
medical | 1.96 | 2.84 | 1.20 |
scene | 1.57 | 1.48 | 2.95 |
yeast | 1.62 | 3.00 | 1.38 |
Total wins | 1 | 3 | 5 |
(d) Macro-average F-measure | |||
Single-JMI | Joint-JMI | Group-JMI-Rand | |
(Our Method) | |||
CAL500 | 1.92 | 2.25 | 1.82 |
emotions | 2.10 | 2.08 | 1.82 |
enron | 2.00 | 1.00 | 3.00 |
genbase | 2.34 | 1.49 | 2.17 |
image | 1.77 | 2.80 | 1.43 |
languagelog | 1.43 | 1.65 | 2.92 |
medical | 2.01 | 2.84 | 1.15 |
scene | 1.77 | 1.30 | 2.92 |
yeast | 1.75 | 3.00 | 1.25 |
Total wins | 1 | 3 | 5 |
Name | Application | Examples | Features | Targets |
---|---|---|---|---|
atp1d | Airline Ticket Price | 337 | 411 | 6 |
atp7d | Airline Ticket Price | 296 | 411 | 6 |
oes97 | Occupational Employment Survey | 334 | 263 | 16 |
oes10 | Occupational Employment Survey | 403 | 298 | 16 |
osales | Online Product Sales | 639 | 413 | 12 |
scm20d | Supply Chain Management | 8966 | 61 | 16 |
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Sechidis, K.; Spyromitros-Xioufis, E.; Vlahavas, I. Information Theoretic Multi-Target Feature Selection via Output Space Quantization. Entropy 2019, 21, 855. https://doi.org/10.3390/e21090855
Sechidis K, Spyromitros-Xioufis E, Vlahavas I. Information Theoretic Multi-Target Feature Selection via Output Space Quantization. Entropy. 2019; 21(9):855. https://doi.org/10.3390/e21090855
Chicago/Turabian StyleSechidis, Konstantinos, Eleftherios Spyromitros-Xioufis, and Ioannis Vlahavas. 2019. "Information Theoretic Multi-Target Feature Selection via Output Space Quantization" Entropy 21, no. 9: 855. https://doi.org/10.3390/e21090855
APA StyleSechidis, K., Spyromitros-Xioufis, E., & Vlahavas, I. (2019). Information Theoretic Multi-Target Feature Selection via Output Space Quantization. Entropy, 21(9), 855. https://doi.org/10.3390/e21090855