Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
High Accuracy Partially Monotone Ordinal Classification

High Accuracy Partially Monotone Ordinal Classification

Reynolds, Mark
Bartley, Christopher
Liu, Wei

Paperback

General Computers

ISBN10: 1636480144
ISBN13: 9781636480145
Publisher: Eliva Pr
Published: Oct 19 2020
Pages: 194
Weight: 0.59
Height: 0.41 Width: 5.98 Depth: 9.02
Language: English
The problem with many machine learning classification algorithms is that their high level of accuracy is achieved at the cost of model comprehensibility, and with a consequent loss of justifiability: their mechanism cannot be shown to be reasonable because it cannot be explained. This has hindered their acceptance in sensitive domains, leading to growing demand for 'explainable AI'. In addition, the EU's recent GDPR legislation has elevated the issue to a legal requirement. If domain knowledge regarding nondecreasing (monotone) relationships could be incorporated into high performance classification algorithms without compromising their performance, the resulting increase in comprehensibility may allow them to surpass 'black box' barriers to acceptance and unlock their high accuracy for wider use.

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