Beyond Class Imbalance: TPT-Robust Cost-Matrix Embedding for Cost-Sensitive Learning

Cost-sensitive classification is critical when prediction errors incur unequal costs, but class-frequency correction and scalar loss reweighting can obscure the directed pairwise structure of the task. A class may be frequent yet safety-critical, and the cost of confusing one class with another need not equal the reverse error. We introduce NICME (Non-Identical Cost-Matrix Embeddings), a cost-sensitive training objective for deep visual classification with non-identical, asymmetric cost matrices. NICME embeds each true-label row of the cost matrix into logit space through pairwise cost margins, together with a normalized expected-cost regularizer on the original predictive distribution. An exact finite-loss characterization shows how above-baseline costs increase the raw-logit separation required from costly wrong classes, including at arbitrarily small positive loss levels. The objective retains ordinary raw-logit inference. Experiments combine balanced binary and multiclass tasks, calibrated minimum-cost decision baselines, an eight-seed component study, and six dense asymmetric cost matrices on CIFAR-100. NICME provides favorable cost–utility operating points on the retained PMI-20 and BreaKHis comparisons and lowers mean raw-argmax cost in five of the six CIFAR-100 settings. Component and sensitivity studies identify the directed margin as a useful mechanism and characterize the role of regularization, calibration, and cost scale.