排序方式： 共有71条查询结果，搜索用时 598 毫秒
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Cross-Company Churn Prediction (CCCP) is a domain of research where one company (target) is lacking enough data and can use data from another company (source) to predict customer churn successfully. To support CCCP, the cross-company data is usually transformed to a set of similar normal distribution of target company data prior to building a CCCP model. However, it is still unclear which data transformation method is most effective in CCCP. Also, the impact of data transformation methods on CCCP model performance using different classifiers have not been comprehensively explored in the telecommunication sector. In this study, we devised a model for CCCP using data transformation methods (i.e., log, z-score, rank and box-cox) and presented not only an extensive comparison to validate the impact of these transformation methods in CCCP, but also evaluated the performance of underlying baseline classifiers (i.e., Naive Bayes (NB), K-Nearest Neighbour (KNN), Gradient Boosted Tree (GBT), Single Rule Induction (SRI) and Deep learner Neural net (DP)) for customer churn prediction in telecommunication sector using the above mentioned data transformation methods. We performed experiments on publicly available datasets related to the telecommunication sector. The results demonstrated that most of the data transformation methods (e.g., log, rank, and box-cox) improve the performance of CCCP significantly. However, the Z-Score data transformation method could not achieve better results as compared to the rest of the data transformation methods in this study. Moreover, it is also investigated that the CCCP model based on NB outperform on transformed data and DP, KNN and GBT performed on the average, while SRI classifier did not show significant results in term of the commonly used evaluation measures (i.e., probability of detection, probability of false alarm, area under the curve and g-mean). 相似文献
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Satish Nargundkar Milind Shrikhande 《Decision Sciences Journal of Innovative Education》2014,12(1):55-72
Student Evaluations of Instruction (SEIs) from about 6,000 sections over 4 years representing over 100,000 students at the college of business at a large public university are analyzed, to study the impact of noninstructional factors on student ratings. Administrative factors like semester, time of day, location, and instructor attributes like gender and rank are studied. The combined impact of all the noninstructional factors studied is statistically significant. Our study has practical implications for administrators who use SEIs to evaluate faculty performance. SEI scores reflect some inherent biases due to noninstructional factors. Appropriate norming procedures can compensate for such biases, ensuring fair evaluations. 相似文献
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蔡义忠 《黄冈职业技术学院学报》2010,12(5):21-23
主要探讨排名算法的原理及在实际应用中的实现。通过区分"田赛"和"径赛"的升降序,实现对比赛成绩的类似于Excel的Rank排名,并与裁判所提交的赛场手工排名进行比较,来判断裁判所提交的纸质数据是否有效,从而具有一定的纠错功能。 相似文献
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文章论述了中专学校图书馆干部队伍现状和问题。提出要转变观念、吸引人才,并留住人才、用好人才,从根本上改善干部队伍结构。参考文献2。 相似文献
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本文采用加权秩和比的评价方法,对1993年全国各省(市)高校的科研综合实力进行了评价和排序,为定量评价科研的综合实力和效益提供一种新的方法. 相似文献
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Measuring effectiveness of information retrieval (IR) systems is essential for research and development and for monitoring search quality in dynamic environments. In this study, we employ new methods for automatic ranking of retrieval systems. In these methods, we merge the retrieval results of multiple systems using various data fusion algorithms, use the top-ranked documents in the merged result as the “(pseudo) relevant documents,” and employ these documents to evaluate and rank the systems. Experiments using Text REtrieval Conference (TREC) data provide statistically significant strong correlations with human-based assessments of the same systems. We hypothesize that the selection of systems that would return documents different from the majority could eliminate the ordinary systems from data fusion and provide better discrimination among the documents and systems. This could improve the effectiveness of automatic ranking. Based on this intuition, we introduce a new method for the selection of systems to be used for data fusion. For this purpose, we use the bias concept that measures the deviation of a system from the norm or majority and employ the systems with higher bias in the data fusion process. This approach provides even higher correlations with the human-based results. We demonstrate that our approach outperforms the previously proposed automatic ranking methods. 相似文献
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讨论n阶方阵A与其对应的高次伴随矩阵A(m)的特征根,根据A的特征根给出了高次伴随矩阵A(m)的特征根的表达式,并利用数学归纳法证明了结论。 相似文献
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汪莉 《中国科技期刊研究》2018,29(4):400-405
【目的】 改进现有百分位数指标在揭示期刊载文量特征方面的不足,构建基于被引频次与载文量双重分布的期刊评价指标PRC。【方法】 以2016版《期刊引证报告》生物学学科的85种期刊为研究样本,通过二八定律筛选核心论文样本集,利用布拉德福区域分析法设计基于文献密度分布的载文量权重,利用百分位数建立基于被引频次分布的引文指标,构建期刊综合影响力评价指标PRC。比较PRC与其他文献计量指标的相关性,验证指标评价效果。【结果】 PRC不仅在一定程度上克服了影响因子的内在缺陷,也能合理揭示期刊的出版规模效应,很好地均衡了相对值和绝对值两类指标,改进了百分位数指标的评价效果。【结论】 PRC指标兼顾被引频次和载文量双重分布,从数量和质量两个维度衡量期刊影响力,可用于评价科技期刊的学术影响力。 相似文献