  Machine learning models are widely used in real-world applications. However,
their complexity makes it often challenging to interpret the rationale behind
their decisions. Counterfactual explanations (CEs) have emerged as a viable
solution for generating comprehensible explanations in eXplainable Artificial
Intelligence (XAI). CE provides actionable information to users on how to
achieve the desired outcome with minimal modifications to the input. However,
current CE algorithms usually operate within the entire feature space when
optimizing changes to turn over an undesired outcome, overlooking the
identification of key contributors to the outcome and disregarding the
practicality of the suggested changes. In this study, we introduce a novel
methodology, that is named as user feedback-based counterfactual explanation
(UFCE), which addresses these limitations and aims to bolster confidence in the
provided explanations. UFCE allows for the inclusion of user constraints to
determine the smallest modifications in the subset of actionable features while
considering feature dependence, and evaluates the practicality of suggested
changes using benchmark evaluation metrics. We conducted three experiments with
five datasets, demonstrating that UFCE outperforms two well-known CE methods in
terms of \textit{proximity}, \textit{sparsity}, and \textit{feasibility}.
Reported results indicate that user constraints influence the generation of
feasible CEs.
