mec: Unsupervised Maximum Entropy Classifier for Record Linkage

Description

Implements several extensions to the maximum entropy classification (MEC) algorithm for record linkage (see Lee et al. (2022)), iteratively estimating probability/density ratios to classify record pairs into matches and non-matches based on comparison vectors.

Usage

mec(
  A,
  B,
  variables,
  comparators = NULL,
  methods = NULL,
  duplicates_in_A = FALSE,
  start_params = NULL,
  nonpar_hurdle = TRUE,
  set_construction = NULL,
  target_rate = 0.03,
  max_iter_bisection = 100,
  tol = 0.005,
  delta = 0.5,
  eps = 0.05,
  max_iter_em = 10,
  tol_em = 1,
  controls_nleqslv = list(),
  controls_kliep = control_kliep(),
  true_matches = NULL
)

Value

Returns a list containing:

M_est -- a data.table with predicted matches,
n_M_est -- estimated classification set size,
flr_est -- estimated false link rate (FLR),
mmr_est -- estimated missing match rate (MMR),
iter_bisection -- the number of iterations in the bisection procedure,
b_vars -- a character vector of variables used for the "binary" method (with the prefix "gamma_"),
cpar_vars -- a character vector of variables used for the "continuous_parametric" method (with the prefix "gamma_"),
cnonpar_vars -- a character vector of variables used for the "continuous_nonparametric" method (with the prefix "gamma_"),
hm_vars -- a character vector of variables used for the "hit_miss" method (with the prefix "gamma_"),
b_params -- parameters estimated using the "binary" method,
cpar_params -- parameters estimated using the "continuous_parametric" method,
hm_params -- parameters estimated using the "hit_miss" method,
ratio_kliep -- a result of the kliep function,
variables -- a character vector of key variables used for comparison,
set_construction -- a method for constructing the predicted set of matches,
eval_metrics -- standard metrics for quality assessment (if true_matches is provided),
confusion -- confusion matrix (if true_matches is provided).

Arguments

A: A duplicate-free data.frame or data.table.
B: A duplicate-free data.frame or data.table.
variables: A character vector of key variables used to create comparison vectors.
comparators: A named list of functions for comparing pairs of records.
methods: A named list of methods used for estimation ("binary", "continuous_parametric", "continuous_nonparametric" or "hit_miss").
duplicates_in_A: Logical indicating whether to allow A to have duplicate records.
start_params: Start parameters for the "binary", "continuous_parametric" and "hit_miss" methods.
nonpar_hurdle: Logical indicating whether to use a hurdle model or not (used only if the "continuous_nonparametric" method has been chosen for at least one variable).
set_construction: A method for constructing the predicted set of matches ("size", "flr" or "mmr").
target_rate: A target false link rate (FLR) or missing match rate (MMR) (used only if set_construction == "flr" or set_construction == "mmr").
max_iter_bisection: A maximum number of iterations for the bisection procedure (used only if set_construction == "flr" or set_construction == "mmr").
tol: Error tolerance in the bisection procedure (used only if set_construction == "flr" or set_construction == "mmr").
delta: A numeric value specifying the tolerance for the change in the estimated number of matches between iterations.
eps: A numeric value specifying the tolerance for the change in model parameters between iterations.
max_iter_em: A maximum number of iterations for the EM algorithm (used only if the "hit_miss" method has been chosen for at least one variable).
tol_em: Error tolerance in the EM algorithm (used only if the "hit_miss" method has been chosen for at least one variable).
controls_nleqslv: Controls passed to the nleqslv function (only if the "continuous_parametric" method has been chosen for at least one variable).
controls_kliep: Controls passed to the kliep function (only if the "continuous_nonparametric" method has been chosen for at least one variable).
true_matches: A data.frame or data.table indicating known matches.

Author

Adam Struzik

Details

Consider two datasets without duplicates: $A$ and $B$. Let the bipartite comparison space $\Omega = A \times B$ consist of matches $M$ and non-matches $U$ between the records in files $A$ and $B$. For any pair of records $(a,b) \in \Omega$, let $\pmb{\gamma}_{ab} = (\gamma_{ab}^1,\gamma_{ab}^2, \ldots,\gamma_{ab}^K)'$ be the comparison vector between a set of key variables. The original MEC algorithm uses the binary comparison function to evaluate record pairs across two datasets. However, this approach may be insufficient when handling datasets with frequent errors across multiple variables.

We propose the use of continuous comparison functions to address the limitations of binary comparison methods. We consider every semi-metric, i.e., a function $d: A \times B \to \mathbb{R}$, satisfying the following conditions:

$d(x,y) \geq 0$,
$d(x,y) = 0$ if and only if $x = y$,
$d(x,y) = d(y,x)$.

For example, we can use $1 - \text{Jaro-Winkler distance}$ for character variables (which is implemented in the automatedRecLin package as the jarowinkler_complement function) or the Euclidean distance for numerical variables. The automatedRecLin package allows the use of a different comparison function for each key variable (which should be specified as a list in the comparators argument). The default function for each key variable is cmp_identical (the binary comparison function).

The mec function offers different approaches to estimate the probability/density ratio between matches and non-matches, which should be specified as a list in the methods argument. The available methods suitable for the binary comparison function are "binary" and "hit_miss". Both assume that $\gamma_{ab}^k|M$ and $\gamma_{ab}^k|U$ follow Bernoulli distributions. "binary" and "hit_miss" both estimate the parameters for the matches iteratively, but "binary" estimates the parameters for the non-matches only at the start, while "hit_miss" does so iteratively using a hit-miss model (for details see Lee et al. (2022)). "binary" is the default method for each variable.

For the continuous semi-metrics we suggest the usage of "continuous_parametric" or "continuous_nonparametric" method. The "continuous_parametric" method assumes that $\gamma_{ab}^k|M$ and $\gamma_{ab}^k|U$ follow hurdle Gamma distributions. The density function of a hurdle Gamma distribution is characterized by three parameters $p_0 \in (0,1)$ and $\alpha, \beta > 0$ as follows: $$ f(x;p_0,\alpha,\beta) = p_0^{\mathbb{I}(x = 0)}[(1 - p_0) v(x;\alpha,\beta)]^{\mathbb{I}(x > 0)}, $$ where $$ v(x;\alpha,\beta) = \frac{\beta^{\alpha} x^{\alpha - 1} \exp(-\beta x)} {\Gamma(\alpha)} $$ is the density function of a Gamma distribution (for details see Vo et al. (2023)). At the beginning, the algorithm estimates the parameters for the non-matches and then does it iteratively for the matches. The "continuous_nonparametric" method does not assume anything about the distributions of the comparison vectors. It iteratively directly estimates the density ratio between the matches and the non-matches, using the Kullback-Leibler Importance Estimation Procedure (KLIEP). For details see Sugiyama et al. (2008).

The mec function allows the construction of the predicted set of matches using its estimated size or the bisection procedure, described in Lee et al. (2022), based on a target False Link Rate (FLR) or missing match rate (MMR). To use the second option, set set_construction = "flr" or set_construction = "mmr" and specify a target error rate using the target_rate argument.

The assumption that $A$ and $B$ contain no duplicate records might be relaxed by allowing $A$ to have duplicates. To do so, set duplicates_in_A = TRUE.

References

Lee, D., Zhang, L.-C. and Kim, J. K. (2022). Maximum entropy classification for record linkage. Survey Methodology, Statistics Canada, Catalogue No. 12-001-X, Vol. 48, No. 1.

Vo, T. H., Chauvet, G., Happe, A., Oger, E., Paquelet, S., and Garès, V. (2023). Extending the Fellegi-Sunter record linkage model for mixed-type data with application to the French national health data system. Computational Statistics & Data Analysis, 179, 107656.

Sugiyama, M., Suzuki, T., Nakajima, S. et al. Direct importance estimation for covariate shift adaptation. Ann Inst Stat Math 60, 699–746 (2008). tools:::Rd_expr_doi("10.1007/s10463-008-0197-x")

Examples

Run this code

df_1 <- data.frame(
  name = c("Emma", "Liam", "Olivia", "Noah", "Ava",
           "Ethan", "Sophia", "Mason", "Isabella", "James"),
  surname = c("Smith", "Johnson", "Williams", "Brown", "Jones",
              "Garcia", "Miller", "Davis", "Rodriguez", "Wilson"),
  city = c("New York", "Los Angeles", "Chicago", "Houston", "Phoenix",
           "Philadelphia", "San Antonio", "San Diego", "Dallas", "San Jose")
)

df_2 <- data.frame(
  name = c(
    "Emma", "Liam", "Olivia", "Noah",
    "Ava", "Ehtan", "Sopia", "Mson",
    "Charlotte", "Benjamin", "Amelia", "Lucas"
  ),
  surname = c(
     "Smith", "Johnson", "Williams", "Brown",
    "Jnes", "Garca", "Miler", "Dvis",
    "Martinez", "Lee", "Hernandez", "Clark"
  ),
  city = c(
    "New York", "Los Angeles", "Chicago", "Houston",
    "Phonix", "Philadelpia", "San Antnio", "San Dieg",
    "Seattle", "Miami", "Boston", "Denver"
  )
)
true_matches <- data.frame(
  "a" = 1:8,
  "b" = 1:8
)

variables <- c("name", "surname", "city")
comparators <- list(
  "name" = jarowinkler_complement(),
  "surname" = jarowinkler_complement(),
  "city" = jarowinkler_complement()
)
methods <- list(
  "name" = "continuous_parametric",
  "surname" = "continuous_parametric",
  "city" = "continuous_parametric"
)

set.seed(1)
result <- mec(A = df_1, B = df_2,
              variables = variables,
              comparators = comparators,
              methods = methods,
              true_matches = true_matches)
result

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