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Questions tagged [fuzzy-logic]

Fuzzy logic is logic handling uncertainty by using as truth values a real number between 0 and 1 to represent less or more certainty about a variable. Fuzzy operators similarly work with degrees of truth.

What is it?

Fuzzy logic is logic handling uncertainty by using as truth values a real number between 0 and 1 to represent less or more certainty about a variable. It is a form of many-valued logic. Fuzzy operators similarly work with degrees of truth.

##How does it work? Fuzzy logic deals with reasoning that is approximate rather than fixed and exact. In contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have a truth value that ranges in degree between 0 and 1. Fuzzy logic has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false.

The reasoning in fuzzy logic is similar to human reasoning. It allows for approximate values and inferences as well as incomplete or ambiguous data (fuzzy data) as opposed to only relying on crisp data (binary yes/no choices). Fuzzy logic is able to process incomplete data and provide approximate solutions to problems other methods find difficult to solve.

In traditional logic, an answer is either black or white. Using fuzzy logic, the answer could be white, black or any variation of grey.

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Levenshtein distance vs simple for loop

I have recently begun studying different data science principles, and have had a particular interest as of late in fuzzy matching. For preface, I'd like to include smarter fuzzy searching in a proprietary language named "4D" in my workplace, so…
Jadon Steinmetz
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Data Matching Using Machine Learning

I have around 4000 customer records and 6000 user records and about 3000 customer records match leaving 1000 unmatched customers. I have created a fuzzy matching algorithm using Levenshtein and Hamming and added weights to certain properties, but I…
Rodrigo Estrella
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Plagiarism detection with Python

Background Using Python, I need to score the existence of a quote, containing around 2-7 words, a longer text. The quote doesn't have to match the text precisely, but similar words should have the same order. For example, given the following long…
Michael
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Check similarity of table/csv of Product Names

We've got a list of approximately 18,000 product names (they're from 80-90 sources, so quite a few that are similar but not duplicates - these were picked as DISTINCT from a table) unfortunately there are different ways of expressing these names. We…
Lisa Anna
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datasets for fuzzy clustering

By now i thought that fuzzy clustering can be applied to any kind of data-sets but now i have heard that it can be applied only to specific data-sets that involve the concept of probability, is that true?
Vivek Mehta
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Fuzzy Rules with more than two variable in python

I am trying to build a fuzzy inference system in Python using skfuzzy library. I have 4 variables depending on which output class is decided. def fuzzInferenceself(): ### Input ### hf_very_poor = fuzz.trimf(hotel_facility, [0, 0.15, 0.3]) …
maggs
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Why does image segmentation benefit from fuzzy clustering?

In many image processing papers, I've seen that they used fuzzy logic for segmentation I wonder how fuzzification impact the result that made Fuzzy-C-Means better than ordinary K-Means. PS. If possible could you provide me the sample data sets for…
tanawatl
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How fit_transform, transform and TfidfVectorizer works

I'm a machine learning beginner and I tried to use the cosine similarity on fuzzy matching purpose. In the following example I want to compare 'data_dirty' with 'data_clean' : When I have to vectorize my data I do not really understand what is the…
nananinanana
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Fuzzily join two large sets of postal addresses

I have two tables of postal address information - the one is about 2 million records, the other roughly 40 million. They have quite bad quality, and also are not quite compatible with each other (different conventions in both sets, some fields cut…
AnoE
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What's the difference between multi label classification and fuzzy classification?

Is it just the between academics and practitioners in term usage? Or is theoretical difference of how we consider each sample: as belonging to multiple classes at once or to one fuzzy class? Or this distinction has some practical meaning of how we…
DmytroSytro
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Can inferencing come from incomplete rule sets?

I have some data for medical diagnosis, consisting of some rules about relationship of diseases and their symptoms, for example disease D1 frequently has symptom S1 or disease D2 rarely has symptom S1. Given some symptoms, I want to somehow…
Dandelion
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Algorithm to determine a single output value based on multiple input values

The main challenge is the lack of data. Input values come from tests results of patients. A patient takes a breath test at an interval during a timespan. The result values can range from 0 to ~200, and can be plotted for diagnose by a doctor based…
human
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Does anyone have any thought how come the fuzzy rules can be verified and validated?

Fuzzy logic was utilized to derive a performance indicator of some manufacturing facilities in an uncertain environment. There are some historical data obtained from some manufacturing facilities’ rigorous assessments. How come can I use this data…
Amir
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Fuzzy and FuzzyWuzzy: what are the differences in text comparison?

I have found a lot of information about fuzzy logic, but less information about fuzzywuzzy. I would like to know more about this, the function which determines the logic, if possible, and understand what partial_ratio in Python does. Any information…
LdM
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What is the generalization of binary/boolean matrix factorization to fuzzy logics called?

Given a matrix of boolean values $\mathbf{X} \in \mathbb{B}^{M \times N} = \{\top, \bot\}^{M \times N}$, the binary/boolean matrix factorization (BMF) problem is to find $\mathbf{U} \in \mathbb{B}^{M \times K}$ and $\mathbf{V} \in \mathbb{B}^{K…
aaronsteven
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