# Fuzzy sets

#### Lluís Revilla

lluis.revilla@gmail.com

#### 2025 de febr. 17

Abstract

Describes the fuzzy sets, interpretation and how to work with them.

# Getting started

This vignettes supposes that you already read the “About BaseSet” vignette. This vignette explains what are the fuzzy sets and how to use them. As all methods for “normal” sets are available for fuzzy sets this vignette focuses on how to create, use them.

# What are fuzzy sets and why/when use them ?

Fuzzy sets are generalizations of classical sets where there is some vagueness, one doesn’t know for sure something on this relationship between the set and the element. This vagueness can be on the assignment to a set and/or on the membership on the set. One way these vagueness arise is when classifying a continuous scale on a categorical scale, for example: when the temperature is 20ºC is it hot or not? If the temperature drops to 15ºC is it warm? When does the switch happen from warm to hot?

In fuzzy set theories the step from a continuous scale to a categorical scale is performed by the membership function and is called fuzzification.

When there is a degree of membership and uncertainty on the membership it is considered a type-2 fuzzy set. We can understand it with using as example a paddle ball, it can be used for tennis or paddle (membership), but until we don’t test it bouncing on the court we won’t be sure (assignment) if it is a paddle ball or a tennis ball. We could think about a ball equally good for tennis and paddle (membership) but which two people thought it is for tennis and the other for paddle.

These voting/rating system is also a common scenario where fuzzy sets arise. When several graders/people need to agree but compromise on a middle ground. One modern example of this is ratting apps where several people vote between 1 and 5 an app and the displayed value takes into consideration all the votes.

As you can see when one does have some vagueness or uncertainty then fuzzy logic is a good choice. There have been developed several logic and methods.

# Creating a fuzzy set

To create a fuzzy set you need to have a column named “fuzzy” if you create it from a `data.frame` or have a named numeric vector if you create it from a `list`. These values are restricted to a numeric value between 0 and 1. The value indicates the strength, membership, truth value (or probability) of the relationship between the element and the set.

    set.seed(4567) # To be able to have exact replicates
    relations <- data.frame(sets = c(rep("A", 5), "B", "C"),
                            elements = c(letters[seq_len(6)], letters[6]),
                            fuzzy = runif(7))
    fuzzy_set <- tidySet(relations)

# Working with fuzzy sets

We can work with fuzzy sets as we do with normal sets. But if you remember that at the end of the previous vignette we used an Important column, now it is already included as fuzzy. This allows us to use this information for union and intersection methods and other operations:

## Union

You can make a union of two sets present on the same object.

    BaseSet::union(fuzzy_set, sets = c("A", "B"))
    #>   elements sets     fuzzy
    #> 1        a  A∪B 0.2309186
    #> 2        b  A∪B 0.7412554
    #> 3        c  A∪B 0.1833834
    #> 4        d  A∪B 0.4221682
    #> 5        e  A∪B 0.5996399
    #> 6        f  A∪B 0.2773313
    BaseSet::union(fuzzy_set, sets = c("A", "B"), name = "D")
    #>   elements sets     fuzzy
    #> 1        a    D 0.2309186
    #> 2        b    D 0.7412554
    #> 3        c    D 0.1833834
    #> 4        d    D 0.4221682
    #> 5        e    D 0.5996399
    #> 6        f    D 0.2773313

We get a new set with all the elements on both sets. There isn’t an element in both sets A and B, so the fuzzy values here do not change.

If we wanted to use other logic we can with provide it with the FUN argument.

    BaseSet::union(fuzzy_set, sets = c("A", "B"), FUN = function(x){sqrt(sum(x))})
    #>   elements sets     fuzzy
    #> 1        a  A∪B 0.4805399
    #> 2        b  A∪B 0.8609619
    #> 3        c  A∪B 0.4282329
    #> 4        d  A∪B 0.6497448
    #> 5        e  A∪B 0.7743642
    #> 6        f  A∪B 0.5266226

There are several logic see for instance `?sets::fuzzy_logic`. You should pick the operators that fit on the framework of your data. Make sure that the defaults arguments of logic apply to your data obtaining process.

## Intersection

However if we do the intersection between B and C we can see some changes on the fuzzy value:

    intersection(fuzzy_set, sets = c("B", "C"), keep = FALSE)
    #>   elements sets     fuzzy
    #> 1        f  B∩C 0.2773313
    intersection(fuzzy_set, sets = c("B", "C"), keep = FALSE, FUN = "mean")
    #>   elements sets     fuzzy
    #> 1        f  B∩C 0.5040313

Different logic on the `union()`, `intersection()`, `complement()`, and `cardinality()`, we get different fuzzy values. Depending on the nature of our fuzziness and the intended goal we might apply one or other rules.

## Complement

We can look for the complement of one or several sets:

    complement_set(fuzzy_set, sets = "A", keep = FALSE)
    #>   elements sets     fuzzy
    #> 1        a   ∁A 0.7690814
    #> 2        b   ∁A 0.2587446
    #> 3        c   ∁A 0.8166166
    #> 4        d   ∁A 0.5778318
    #> 5        e   ∁A 0.4003601

Note that the values of the complement are `1-fuzzy` but can be changed:

    filter(fuzzy_set, sets == "A")
    #>   elements sets     fuzzy
    #> 1        a    A 0.2309186
    #> 2        b    A 0.7412554
    #> 3        c    A 0.1833834
    #> 4        d    A 0.4221682
    #> 5        e    A 0.5996399
    complement_set(fuzzy_set, sets = "A", keep = FALSE, FUN = function(x){1-x^2})
    #>   elements sets     fuzzy
    #> 1        a   ∁A 0.9466766
    #> 2        b   ∁A 0.4505405
    #> 3        c   ∁A 0.9663705
    #> 4        d   ∁A 0.8217740
    #> 5        e   ∁A 0.6404320

## Subtract

This is the equivalent of `setdiff`, but clearer:

    subtract(fuzzy_set, set_in = "A", not_in = "B", keep = FALSE, name = "A-B")
    #>   elements sets     fuzzy
    #> 1        a  A-B 0.2309186
    #> 2        b  A-B 0.7412554
    #> 3        c  A-B 0.1833834
    #> 4        d  A-B 0.4221682
    #> 5        e  A-B 0.5996399
    # Or the opposite B-A, but using the default name:
    subtract(fuzzy_set, set_in = "B", not_in = "A", keep = FALSE)
    #>   elements sets     fuzzy
    #> 1        f  B∖A 0.2773313

Note that here there is also a subtraction of the fuzzy value.

# Sizes

If we consider the fuzzy values as probabilities then the size of a set is not fixed. To calculate the size of a given set we have `set_size()`:

    set_size(fuzzy_set)
    #>    sets size probability
    #> 1     A    0 0.037593610
    #> 2     A    1 0.211200558
    #> 3     A    2 0.384150899
    #> 4     A    3 0.278302802
    #> 5     A    4 0.080805868
    #> 6     A    5 0.007946263
    #> 7     B    0 0.722668660
    #> 8     B    1 0.277331340
    #> 9     C    0 0.269268826
    #> 10    C    1 0.730731174

Or an element can be in 0 sets:

    element_size(fuzzy_set)
    #>    elements size probability
    #> 1         a    0   0.7690814
    #> 2         a    1   0.2309186
    #> 3         b    0   0.2587446
    #> 4         b    1   0.7412554
    #> 5         c    0   0.8166166
    #> 6         c    1   0.1833834
    #> 7         d    0   0.5778318
    #> 8         d    1   0.4221682
    #> 9         e    0   0.4003601
    #> 10        e    1   0.5996399
    #> 11        f    0   0.1945921
    #> 12        f    1   0.6027532
    #> 13        f    2   0.2026547

In this example we can see that it is more probable that the element “a” is not present than the element “f” being present in one set.

# Interpretation

Sometimes it can be a bit hard to understand what do the fuzzy sets mean on your analysis. To better understand let’s dive a bit in the interpretation with an example:

Imagine you have your experiment where you collected data from a sample of cells for each cell (our elements). Then you used some program to classify which type of cell it is (alpha, beta, delta, endothelial), this are our sets. The software returns a probability for each type it has: the higher, the more confident it is of the assignment:

    sc_classification <- data.frame(
        elements = c("D2ex_1", "D2ex_10", "D2ex_11", "D2ex_12", "D2ex_13", "D2ex_14",
                     "D2ex_15", "D2ex_16", "D2ex_17", "D2ex_18", "D2ex_1", "D2ex_10",
                     "D2ex_11", "D2ex_12", "D2ex_13", "D2ex_14", "D2ex_15", "D2ex_16",
                     "D2ex_17", "D2ex_18", "D2ex_1", "D2ex_10", "D2ex_11", "D2ex_12",
                     "D2ex_13", "D2ex_14", "D2ex_15", "D2ex_16", "D2ex_17", "D2ex_18",
                     "D2ex_1", "D2ex_10", "D2ex_11", "D2ex_12", "D2ex_13", "D2ex_14",
                     "D2ex_15", "D2ex_16", "D2ex_17", "D2ex_18"),
        sets = c("alpha", "alpha", "alpha", "alpha", "alpha", "alpha", "alpha",
                 "alpha", "alpha", "alpha", "endothel", "endothel", "endothel",
                 "endothel", "endothel", "endothel", "endothel", "endothel",
                 "endothel", "endothel", "delta", "delta", "delta", "delta", "delta",
                 "delta", "delta", "delta", "delta", "delta", "beta", "beta", "beta",
                 "beta", "beta", "beta", "beta", "beta", "beta", "beta"),
        fuzzy = c(0.18, 0.169, 0.149, 0.192, 0.154, 0.161, 0.169, 0.197, 0.162, 0.201,
                  0.215, 0.202, 0.17, 0.227, 0.196, 0.215, 0.161, 0.195, 0.178,
                  0.23, 0.184, 0.172, 0.153, 0.191, 0.156, 0.167, 0.165, 0.184,
                  0.162, 0.194, 0.197, 0.183, 0.151, 0.208, 0.16, 0.169, 0.169,
                  0.2, 0.154, 0.208), stringsAsFactors = FALSE)
    head(sc_classification)
    #>   elements  sets fuzzy
    #> 1   D2ex_1 alpha 0.180
    #> 2  D2ex_10 alpha 0.169
    #> 3  D2ex_11 alpha 0.149
    #> 4  D2ex_12 alpha 0.192
    #> 5  D2ex_13 alpha 0.154
    #> 6  D2ex_14 alpha 0.161

Our question is which type of cells did we have on the original sample?

We can easily answer this by looking at the relations that have higher confidence of the relationship for each cell.

    sc_classification %>%
        group_by(elements) %>%
        filter(fuzzy == max(fuzzy)) %>%
        group_by(sets) %>%
        count()
    #> # A tibble: 3 × 2
    #> # Groups:   sets [3]
    #>   sets         n
    #>   <chr>    <int>
    #> 1 alpha        1
    #> 2 beta         2
    #> 3 endothel     8

There is a cell that can be in two cell types, because we started with 10 cells and we have 11 elements here. However, how likely is that a cell is placed to just a single set?

    scTS <- tidySet(sc_classification) # Conversion of format
    sample_cells <- scTS %>%
        element_size() %>%
        group_by(elements) %>%
        filter(probability == max(probability))
    sample_cells
    #> # A tibble: 10 × 3
    #> # Groups:   elements [10]
    #>    elements  size probability
    #>    <chr>    <dbl>       <dbl>
    #>  1 D2ex_1       0       0.422
    #>  2 D2ex_10      0       0.449
    #>  3 D2ex_11      0       0.508
    #>  4 D2ex_12      1       0.412
    #>  5 D2ex_13      0       0.482
    #>  6 D2ex_14      0       0.456
    #>  7 D2ex_15      0       0.484
    #>  8 D2ex_16      0       0.422
    #>  9 D2ex_17      0       0.488
    #> 10 D2ex_18      1       0.414

There must be some cell misclassification: we have 10 cells in this example but there are 2 cells that the maximum probability predicted for these types is a single cell type. Even the cell that had two equal probabilities for two cell types is more probable to be in no one of these cell types than in any of them.

Ideally the predicted number of cells per type and the cells with higher confidence about the type should match.

We can also look the other way around: How good is the prediction of a cell type for each cell?

    scTS %>%
        set_size() %>%
        group_by(sets) %>%
        filter(probability == max(probability))
    #> # A tibble: 4 × 3
    #> # Groups:   sets [4]
    #>   sets      size probability
    #>   <chr>    <dbl>       <dbl>
    #> 1 alpha        1       0.313
    #> 2 beta         1       0.302
    #> 3 delta        1       0.313
    #> 4 endothel     2       0.303

We can see that for each cell type it is probable to have at least one cell and in the endothelial cell type two cells is the most probable outcome. However, these probabilities are lower than the probabilities of cells being assigned a cell type. This would mean that this method is not a good method or that the cell types are not specific enough for the cell.

In summary, the cells that we had most probable are not those 4 cell types except in two cells were it might be.

# Session info

    #> R Under development (unstable) (2025-02-14 r87718)
    #> Platform: x86_64-pc-linux-gnu
    #> Running under: Ubuntu 24.04.2 LTS
    #>
    #> Matrix products: default
    #> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
    #>
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    #>  [3] LC_TIME=ca_ES.UTF-8        LC_COLLATE=C
    #>  [5] LC_MONETARY=ca_ES.UTF-8    LC_MESSAGES=en_US.UTF-8
    #>  [7] LC_PAPER=ca_ES.UTF-8       LC_NAME=C
    #>  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    #> [11] LC_MEASUREMENT=ca_ES.UTF-8 LC_IDENTIFICATION=C
    #>
    #> time zone: Europe/Madrid
    #> tzcode source: system (glibc)
    #>
    #> attached base packages:
    #> [1] stats4    stats     graphics  grDevices utils     datasets  methods
    #> [8] base
    #>
    #> other attached packages:
    #>  [1] reactome.db_1.89.0   forcats_1.0.0        ggplot2_3.5.1
    #>  [4] GO.db_3.20.0         org.Hs.eg.db_3.20.0  AnnotationDbi_1.69.0
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