This paper proposes a unified approach based on maximal consistent blocks (MCBs) to address the problem of incomplete single-label and multi-label dimensional reduction. The matrix computation method for maximal consistent blocks is improved by introducing a dynamic multi-row detection mechanism and optimizing the block size determination criteria. The complete set of maximal consistent blocks can be efficiently obtained via matrix intersection operations. For incomplete single-label decision information systems, an attribute reduction algorithm is designed based on maximal consistent blocks. Redundant attributes are eliminated by preserving the upper and lower approximation distributions of decision classes. In the multi-label scenario, a complementary decision reduct method integrating coarse and fine decision functions is proposed, and a unified solution paradigm is adopted to accomplish multi-label dimensional reduction. The effectiveness in classification (F1-score, Ranking Loss, Hamming Loss), reduction performance, and runtime efficiency is validated via statistical tests, scalability studies, structured missingness studies, and comparisons with four representative baselines on Birds, Scene, and Yeast datasets (5%/10%/15% missing rates).

Semi-supervised fine-grained classification suffers from accumulating pseudo-label errors among potentially confusable classes when labeled data are scarce, yet negative-rule information that excludes classes at the attribute level remains unused. We propose AW-RS-ABL, a semi-supervised abductive learning method that fuses intra-class and inter-class attribute importance within rough sets and couples positive-rule confidence matching with negative-rule candidate-set reduction for pseudo-label correction. Samples, attributes, and labels form a decision information system $S=(U,A,F,d)$. Inter-class discriminative power is measured by positive-region dependency ${\gamma }_A\left(d\right)$ and intra-class stability by class-conditional entropy $H(a\mid d)$; the two are normalized and linearly fused into an attribute weight that selects a key attribute subset $A^{*}$. Weighted positive rules and high-confidence negative rules are built on $A^{*}$: negative rules shrink the candidate class set, and positive rules complete the correction via confidence matching. Rule consistency checking is embedded in the semi-supervised pseudo-label loop so that rule verification and perceptual model prediction co-evolve across training iterations. On six datasets spanning SAR target recognition, medical diagnosis, and agricultural classification at a reported 10% label ratio, AW-RS-ABL has the highest reported mean top-1 accuracy in each comparison, with displayed mean differences of 1.36–2.58 percentage points relative to FixMatch-Attr. Exact paired Wilcoxon tests with Holm correction support the differences for five datasets, whereas the difference for Dermatology is not significant. On the imbalanced Nursery test set, a representative seed yields 87.34% accuracy but 73.54% macro-F1 and 18.18% recall for the minority class. The reported formulation relies on conditional attributes, and its results remain conditional on their representation and binarization.