The Impact of Partial Balance of Imbalanced Dataset on Classification Performance

The imbalance of network data seriously affects the classification performance of algorithms. Most studies have only used a rough description of data imbalance with less exploration of the specific factors affecting classification performance, which has resulted in difficulty putting forward targeted solutions. In this paper, we find that the impact of medium categories on classification performance cannot be ignored, and therefore propose the concept of partial balance, consisting of Class Number of Partial Balance (β) and Balance Degree of Partial Samples (μ). Combined with Global Slope (α), a parameterized model is established to describe the difference of imbalanced datasets. Experiments are performed on the Moore Dataset and CICIDS 2017 Dataset. The experiment’s results on Random Forest, Decision Tree and Deep Neural Network show increasing α is a conducive step in the performance improvement of minority classes and overall classes. When β of dominant categories increases, that of inferior classes decreases, which results in a decrease in the average performance of minority classes. The lower μ is, the closer the sample size of medium classes is to the minority classes, and the better the average performance is. Based on the conclusions, we propose and verify some basic strategies by various classical algorithms

Combined soft templating with thermal exfoliation toward synthesis of porous g-C3N4 nanosheets for improved photocatalytic hydrogen evolution

Insufficient active sites and fast charge carrier recombination are detrimental to photocatalytic activity of graphitic carbon nitride (g-C3N4). In this work, a combination of pore creating with thermal exfoliation was employed to prepare porous g-C3N4 nanosheets for photocatalytic water splitting into hydrogen. Hexadecyl trimethyl ammonium chloride (CTAC) as the soft template promoted the formation of porous g-C3N4 during the thermal condensation of melamine. On further post-synthesis calcination, the porous g-C3N4 aggregates were exfoliated into discrete nanosheets, accompanied by an increase in specific surface area and defects. Optimal porous g-C3N4 nanosheets achieved 3.6 times the photocatalytic hydrogen evolution rate for bulk counterpart. The enhanced photocatalytic activity may be ascribed to TCN-1%CTAC has larger specific surface area, stronger optical absorption intensity and higher photogenerated electron–hole separation efficiency. The external quantum efficiency of TCN-1%CTAC was measured to be 3.4% at 420 nm. This work provides a simple combinatorial strategy for the preparation of porous g-C3N4 nanosheets with low cost, environmental friendliness and enhanced photocatalytic activity