A Novel Method of Interestingness Measures for Association Rules Mining Based on Profit

Association rules mining is an important topic in the domain of data mining and knowledge discovering. Some papers have presented several interestingness measure methods; the most typical are Support, Confidence, Lift, Improve, and so forth. But their limitations are obvious, like no objective criterion, lack of statistical base, disability of defining negative relationship, and so forth. This paper proposes three new methods, Bi-lift, Bi-improve, and Bi-confidence, for Lift, Improve, and Confidence, respectively. Then, on the basis of utility function and the executing cost of rules, we propose interestingness function based on profit (IFBP) considering subjective preferences and characteristics of specific application object. Finally, a novel measure framework is proposed to improve the traditional one through experimental analysis. In conclusion, the new methods and measure framework are prior to the traditional ones in the aspects of objective criterion, comprehensive definition, and practical application

Creating Ensemble Classifiers with Information Entropy Diversity Measure

Ensemble classifiers improve the classification accuracy by incorporating the decisions made by its component classifiers. Basically, there are two steps to create an ensemble classifier: one is to generate base classifiers and the other is to align the base classifiers to achieve maximum accuracy integrally. One of the major problems in creating ensemble classifiers is the classification accuracy and diversity of the component classifiers. In this paper, we propose an ensemble classifier generating algorithm to improve the accuracy of an ensemble classification and to maximize the diversity of its component classifiers. In this algorithm, information entropy is introduced to measure the diversity of component classifiers, and a cyclic iterative optimization selection tactic is applied to select component classifiers from base classifiers, in which the number of component classifiers is dynamically adjusted to minimize system cost. It is demonstrated that our method has an obvious lower memory cost with higher classification accuracy compared with existing classifier methods

Simultaneous Formation of Interphases on both Positive and Negative Electrodes in High‐Voltage Aqueous Lithium‐Ion Batteries

The formation of solid-electrolyte interphase (SEI) in “water-in-salt” electrolyte (WiSE) expands the electrochemical stability window of aqueous electrolytes beyond 3.0 V. However, the parasitic hydrogen evolution reaction that drives anode corrosion, cracking, and the subsequent reformation of SEI still occurs, compromising long-term cycling performance of the batteries. To improve cycling stability, an unsaturated monomer acrylamide (AM) is introduced as an electrolyte additive, whose presence in WiSE reduces its viscosity and improves ionic conductivity. Upon charging, AM electropolymerizes into polyacrylamide, as confirmed both experimentally and computationally. The in situ polymer constitutes effective protection layers at both anode and cathode surfaces, and enables LiMn2O4||L-TiO2 full cells with high specific capacity (157 mAh g−1 at 1 C), long-term cycling stability (80% capacity retention within 200 cycles at 1 C), and high rate capability (79 mAh g−1 at 30 C). The in situ electropolymerization found in this work provides an alternative and highly effective strategy to design protective interphases at the negative and positive electrodes for high-voltage aqueous batteries of lithium-ion or beyond

“Water‐in‐Eutectogel” Electrolytes for Quasi‐Solid‐State Aqueous Lithium‐Ion Batteries

The development of high safety lithium-ion batteries (LIBs) is greatly impeded by the flammability and leakage concerns of typical organic solvent-based electrolytes. As one of the alternative classes of electrolytes, hydrogel electrolytes exhibit high safety, high flexibility, low cost, and are benign to the environment. However, the narrow electrochemical stability window (ESW) of typical hydrogel electrolytes restricts the operating voltage of battery cells. Here, a new class of “water-in-eutectogel (WiETG)” electrolyte is reported, fabricated by combining a hydrogel with a “deep eutectic solvent” (LiTFSI in acetamide). The obtained WiETG electrolyte exhibits non-flammability, high ionic conductivity, and a wide ESW. LiMn2O4||Li4Ti5O12 cells with the WiETG electrolyte exhibit good cycling stability, high flexibility, and high safety. This newly developed WiETG electrolyte not only broadens the ESW of typical hydrogel electrolytes, but also opens a new perspective on future directions and guidance for the design of high safety electrolytes for flexible LIBs and beyond