商業知識的發展突飛猛進，其中，預測模型在眾多商業智慧中扮演重要的角色，然而，當我們從大量資料萃取隱藏、未知與潛在具有實用性的資訊處理過程時，往往會遇到許多資料品質上的問題而難以著手分析，尤其是遺失值 (Missing value)的問題在資料前置處理階段更是常見的困難。因此，要如何在建立預測模型時有效的處理遺失值是一個很重要的議題。 過去已有許多文獻致力於遺失值處理的議題，其中，Active Feature-Value Acquisition的相關研究更針對訓練資料的選填順序深入探討。Active Feature-Value Acquisition的概念是從具有遺失值的訓練資料中，選擇適當的遺失資料填補，讓預測的模型在最具效率的情況下達到理想的準確率。本研究將延續Active Feature-Value Acquisition的研究主軸，優先考量決策樹上的節點為遺失值選值填補的順序，提出一個新的訓練資料遺失值的選填順序方法─I Sampling，並透過實際的數據進行訓練與測試，同時我們也與過去文獻所提出的方法進行比較，了解不同的填值偵測與順序的選擇對於一個預測模型的分類準確率是否有影響，並了解各個方法的優缺點與在不同情境下的適用性。 本研究所提出的新方法與驗證的結果，將可給予未來從事預測行為的管理或學術工作一些參考與建議，可以依據不同性質的資料採取合宜的選值方式，以節省取值的成本並提高預測模型的分類能力。 / The importance of business intelligence is accelerated developing nowadays. Especially predictive models play a key role in numerous business intelligence tasks. However, while we extract information from unidentified data, there are critical problems of how to handle the missing values, especially in the data pre-processing phase. Therefore, it is important to identify which methods best deal with the missing data when building predictive models. There are several papers dedicated in the research of strategies to deal with the missing values. The topic of Active-Feature Acquisition (aka. AFA) especially worked on the priority order of choosing which feature-value to acquire. The goal of AFA is to reduce the costs of achieving a desired model accuracy by identifying instances for which obtaining complete information is most informative. Followed by the AFA concept, we present an approach- I Sampling, in which feature-values are selected for acquisition based on the attribute on the top node of the current decision tree. Also we compare our approach with other methods in different situations and data missing patterns. Experimental results demonstrate that our approach can induce accurate models using substantially fewer feature-value acquisitions as compared to alternative policies in some situations. The method we proposed can aid the further predictive works in academic and business area. They can therefore choose the right method based on their needs and obtain the informative data in an efficient way.

abstract: Public health surveillance is a special case of the general problem where counts (or rates) of events are monitored for changes. Modern data complements event counts with many additional measurements (such as geographic, demographic, and others) that comprise high-dimensional covariates. This leads to an important challenge to detect a change that only occurs within a region, initially unspecified, defined by these covariates. Current methods are typically limited to spatial and/or temporal covariate information and often fail to use all the information available in modern data that can be paramount in unveiling these subtle changes. Additional complexities associated with modern health data that are often not accounted for by traditional methods include: covariates of mixed type, missing values, and high-order interactions among covariates. This work proposes a transform of public health surveillance to supervised learning, so that an appropriate learner can inherently address all the complexities described previously. At the same time, quantitative measures from the learner can be used to define signal criteria to detect changes in rates of events. A Feature Selection (FS) method is used to identify covariates that contribute to a model and to generate a signal. A measure of statistical significance is included to control false alarms. An alternative Percentile method identifies the specific cases that lead to changes using class probability estimates from tree-based ensembles. This second method is intended to be less computationally intensive and significantly simpler to implement. Finally, a third method labeled Rule-Based Feature Value Selection (RBFVS) is proposed for identifying the specific regions in high-dimensional space where the changes are occurring. Results on simulated examples are used to compare the FS method and the Percentile method. Note this work emphasizes the application of the proposed methods on public health surveillance. Nonetheless, these methods can easily be extended to a variety of applications where counts (or rates) of events are monitored for changes. Such problems commonly occur in domains such as manufacturing, economics, environmental systems, engineering, as well as in public health. / Dissertation/Thesis / Ph.D. Industrial Engineering 2010

abstract: Supply chains are increasingly complex as companies branch out into newer products and markets. In many cases, multiple products with moderate differences in performance and price compete for the same unit of demand. Simultaneous occurrences of multiple scenarios (competitive, disruptive, regulatory, economic, etc.), coupled with business decisions (pricing, product introduction, etc.) can drastically change demand structures within a short period of time. Furthermore, product obsolescence and cannibalization are real concerns due to short product life cycles. Analytical tools that can handle this complexity are important to quantify the impact of business scenarios/decisions on supply chain performance. Traditional analysis methods struggle in this environment of large, complex datasets with hundreds of features becoming the norm in supply chains. We present an empirical analysis framework termed Scenario Trees that provides a novel representation for impulse and delayed scenario events and a direction for modeling multivariate constrained responses. Amongst potential learners, supervised learners and feature extraction strategies based on tree-based ensembles are employed to extract the most impactful scenarios and predict their outcome on metrics at different product hierarchies. These models are able to provide accurate predictions in modeling environments characterized by incomplete datasets due to product substitution, missing values, outliers, redundant features, mixed variables and nonlinear interaction effects. Graphical model summaries are generated to aid model understanding. Models in complex environments benefit from feature selection methods that extract non-redundant feature subsets from the data. Additional model simplification can be achieved by extracting specific levels/values that contribute to variable importance. We propose and evaluate new analytical methods to address this problem of feature value selection and study their comparative performance using simulated datasets. We show that supply chain surveillance can be structured as a feature value selection problem. For situations such as new product introduction, a bottom-up approach to scenario analysis is designed using an agent-based simulation and data mining framework. This simulation engine envelopes utility theory, discrete choice models and diffusion theory and acts as a test bed for enacting different business scenarios. We demonstrate the use of machine learning algorithms to analyze scenarios and generate graphical summaries to aid decision making. / Dissertation/Thesis / Ph.D. Industrial Engineering 2012

Software product development companies are increasingly striving to become data-driven. The access to customer feedback and product data has been, with products increasingly becoming connected to the Internet, demonetized. Systematically collecting the feedback and efficiently using it in product development, however, are challenges that large-scale software development companies face today when being faced by large amounts of available data. In this thesis, we explore the collection, use and impact of customer feedback on software product development. We base our work on a 2-year longitudinal multiple-case study research with case companies in the software-intensive domain, and complement it with a systematic review of the literature. In our work, we identify and confirm that large-software companies today collect vast amounts of feedback data, however, struggle to effectively use it. And due to this situation, there is a risk of prioritizing the development of features that may not deliver value to customers. Our contribution to this problem is threefold. First, we present a comprehensive and systematic review of activities and techniques used to collect customer feedback and product data in software product development. Next, we show that the impact of customer feedback evolves over time, but due to the lack of sharing of the collected data, companies do not fully benefit from this feedback. Finally, we provide an improvement framework for practitioners and researchers to use the collected feedback data in order to differentiate between different feature types and to model feature value during the lifecycle. With our contributions, we aim to bring software companies one step closer to data-driven decision making in software product development.