\begin{algorithmic}[1]
    \State $\mathcal{F} \leftarrow \textsc{makePF}(\{\{r\} | r \in \mathcal{R}\})$
    \State converged $\leftarrow \texttt{false}$
    \While{\texttt{not} converged}
        \State converged $\leftarrow \texttt{true}$
        \State $\mathcal{F}_0 \leftarrow \textsc{SSF}(\mathcal{F}, k)$
        \State $\mathcal{F}' \leftarrow \emptyset$
        \For{$S \in \mathcal{F}_0$}
            \For{$r \in \mathcal{R}$}
                \State $S' \leftarrow S \cup \{r\}$
                \State $\mathcal{F}' \leftarrow \mathcal{F}' \cup \{S'\}$
            \EndFor
        \EndFor
        \State $\mathcal{F}' \leftarrow \textsc{makePF}(\mathcal{F}' \cup \mathcal{F})$
        \State $\texttt{converged} \leftarrow \mathcal{F}' == \mathcal{F}$
        \State $\mathcal{F} \leftarrow \mathcal{F}'$
    \EndWhile
    \Return $\mathcal{F}$
\end{algorithmic}

\begin{algorithmic}
[1]
\State $\mathcal{F} \leftarrow \textsc{makePF}(\{\{r\} | r \in \mathcal{R}\})$
\State converged $\leftarrow \texttt{false}$
\While{\texttt{not} converged}
\State converged $\leftarrow \texttt{true}$
\State $\mathcal{F}_0 \leftarrow \textsc{SSF}(\mathcal{F}, k)$
\State $\mathcal{F}' \leftarrow \emptyset$
\For{$S \in \mathcal{F}_0$}
\For{$r \in \mathcal{R}$}
\State $S' \leftarrow S \cup \{r\}$
\State $\mathcal{F}' \leftarrow \mathcal{F}' \cup \{S'\}$
\EndFor
\EndFor
\State $\mathcal{F}' \leftarrow \textsc{makePF}(\mathcal{F}' \cup \mathcal{F})$
\State $\texttt{converged} \leftarrow \mathcal{F}' == \mathcal{F}$
\State $\mathcal{F} \leftarrow \mathcal{F}'$
\EndWhile
\Return $\mathcal{F}$
\end{algorithmic}
{
    "title": "on finding bi-objective pareto-optimal fraud prevention rule sets for   fintech applications",
    "id": "2311.00964",
    "abstract": "rules are widely used in fintech institutions to make fraud prevention decisions, since rules are highly interpretable thanks to their intuitive if-then structure. in practice, a two-stage framework of fraud prevention decision rule set mining is usually employed in large fintech institutions. this paper is concerned with finding high-quality rule subsets in a bi-objective space (such as precision and recall) from an initial pool of rules. to this end, we adopt the concept of pareto optimality and aim to find a set of non-dominated rule subsets, which constitutes a pareto front. we propose a heuristic-based framework called pors and we identify that the core of pors is the problem of solution selection on the front (ssf). we provide a systematic categorization of the ssf problem and a thorough empirical evaluation of various ssf methods on both public and proprietary datasets. we also introduce a novel variant of sequential covering algorithm called spectralrules to encourage the diversity of the initial rule set and we empirically find that spectralrules further improves the quality of the found pareto front. on two real application scenarios within alipay, we demonstrate the advantages of our proposed methodology compared to existing work.",
    "categories": "cs.lg q-fin.st",
    "doi": "",
    "created": "2023-11-01",
    "updated": "2024-01-17",
    "authors": [
        "chengyao wen",
        "yin lou"
    ],
    "affiliation": [],
    "url": "https://arxiv.org/abs/2311.00964"
}
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