Classified ad sites routinely process hundreds of thousands to millions of posted ads, and only a small percentage of those may be fraudulent. Online scammers often go through a great amount of effort to make their listings look legitimate. Examples include copying existing advertisements from other services, tunneling through local proxies, and even paying for extra services using stolen account information. This paper focuses on applying knowledge discovery concepts towards the detection of online, classified fraud. Traditional data mining is used to extract relevant attributes from an online classified advertisements database and machine learning algorithms are applied to discover patterns and relationships of fraudulent activity. With our proposed approach, we will demonstrate the effectiveness of applying data mining techniques towards the detection of fraud in online classified advertisements.

A very promising tool for data mining and bioinformatics is ensemble gene (feature) selection. Ensemble feature selection is the process of performing multiple runs of feature selection and then aggregating the results into a final ranked list. However, a central question of ensemble feature selection is how to aggregate the individual results into a single ranked feature list. There are a number of techniques available, ranging from simple to complex; the question is which one to choose. This paper is a comprehensive study on the use of nine different rank aggregation techniques for building classification models to use gene microarray data for distinguishing between cancerous and non-cancerous cells (or between patients who did or did not respond well to cancer treatment). The techniques are tested using an ensemble with twenty-five feature selection techniques and fifty iterations along with eleven bioinformatics datasets and five learners. Our results show that Lowest Rank is the worst performing aggregation technique by a clear margin. The other techniques perform similarly well and a simple technique (e.g., Mean aggregation) is preferable due to computation time and the limited possible benefit of a more complex technique. To our knowledge there has never been a study this intensive on the classification abilities of rank aggregation techniques in the field of bioinformatics.

We present an approach that uses learning from demonstration in a computer role playing game to create a controller for a companion team member. We describe a behavior engine that uses case-based reasoning. The behavior engine accepts observation traces of human playing decisions and produces a sequence of actions which can then be carried out by an artificial agent within the gaming environment. Our work focuses on team-based role playing games, where the agents produced by the behavior engine act as team members within a mixed human-agent team. We present the results of a study we conducted, where we assess both the quantitative and qualitative performance difference between human-only teams compared with hybrid human-agent teams. The results of our study show that human-agent teams were more successful at task completion and, for some qualitative dimensions, hybrid teams were perceived more favorably than human-only teams.

One of the major challenges in bioinformatics is selecting the appropriate genes for a given problem, and moreover, choosing the best gene selection technique for this task. Many such techniques have been developed, each with its own characteristics and complexities. Recently, some works have addressed this by introducing ensemble gene selection, which is the process of performing multiple runs of gene selection and aggregating the results into a single final list. The question is, will ensemble gene selection improve the results over those obtained when using single gene selection techniques (e.g., filter-based gene selection techniques on their own without any ensemble approach)? We compare how five filter-based feature (gene) selection techniques work with and without a data diversity ensemble approach (using a single feature selection technique on multiple sampled datasets created from an original one) when used for building models to label cancerous cells (or predict cancer treatment response) based on gene expression levels. Eleven bioinformatics (gene microarray) datasets are employed, along with four feature subset sizes and five learners. Our results show that the techniques Fold Change Ratio and Information Gain will produce better classification results when an ensemble approach is applied, while Probability Ratio and Signal-to-Noise will, in general, perform better without the ensemble approach. For the Area Under the ROC (Receiver Operating Characteristics) Curve ranker, the classification results are similar with or without the ensemble approach. This is, to our knowledge, the first paper to comprehensively examine the difference between the ensemble and single approaches for gene selection in the biomedical and bioinformatics domains.

National Cancer Institute's (NCI) Cancer Information Service (CIS) offers online instant messaging based information service called LiveHelp to patients, family members, friends, and other cancer information consumers. A cancer information specialist (IS) 'chats' with a consumer and provides information on a variety of topics including clinical trials. After a LiveHelp chat session is finished, the IS codes about 20 different elements of metadata about the session in electronic contact record forms (ECRF), which are to be later used for quality control and reporting. Besides straightforward elements like age and gender, more specific elements to be coded include the purpose of contact, the subjects of interaction, and the different responses provided to the consumer, the latter two often taking on multiple values. As such, ECRF coding is a time consuming task and automating this process could help ISs to focus more on their primary goal of helping consumers with valuable cancer related information. As a first attempt in this task, we explored multi-label and multi-class text classification approaches to code the purpose, subjects of interaction, and the responses provided based on the chat transcripts. With a sample dataset of about 673 transcripts, we achieved example-based F-scores of 0.67 (for subjects) and 0.58 (responses). We also achieved label-based micro F-scores of 0.65 (for subjects), 0.62 (for responses), and 0.61 (for purpose). To our knowledge this is the first attempt in automatic coding of LiveHelp transcripts and our initial results on the smaller corpus indicate promising future directions in this task.

Introduction to the papers presented at the 2013 FLAIRS Conference Special Track on Artificial Intelligence in 
Healthcare Informatics.

Currently, signature based Intrusion Detection Systems (IDS) approaches are inadequate to address threats posed to networked systems by zero-day exploits. Statistical machine learning techniques offer a great opportunity to mitigate these threats. However, at this point, statistical based IDS systems are not mature enough to be implemented in realtime systems and the techniques to be used are not sufficiently understood. This study focuses on a recently expanded corpus for IDS analysis. Feature analysis and Support Vector Machines classification are performed to obtain a better understanding of the corpus and to establish a baseline set of results which can be used by other studies for comparison. Results of the classification and feature analysis are discussed.

Named-Entity Recognizers (NERs) are an important part of information extraction systems in annotation tasks. Although substantial progress has been made in recognizing domain-independent named entities (e.g. location, organization and person), there is a need to recognize named entities for domain-specific applications in order to extract relevant concepts. Due to the growing need for smart health applications in order to address some of the latest worldwide epidemics of behavioral issues (e.g. over eating, lack of exercise, alcohol and drug consumption), we focused on the domain of behavior change, especially {\em lifestyle change}. To the best of our knowledge, there is no named-entity recognizer designed for the lifestyle change domain to enable applications to recognize relevant concepts. We describe the design of an ontology for behavioral health based on which we developed a NER augmented with lexical resources. Our NER automatically tags words and phrases in sentences with relevant (lifestyle) domain-specific tags (e.g. [un/]healthy food, potentially-risky/healthy activity, drug, tobacco and alcoholic beverage). We discuss the evaluation that we conducted with with manually collected test data. In addition, we discuss how our ontology enables systems to make further information acquisition for the recognized named entities by using semantic reasoners.

News reportage is intended to serve the public in terms of nurturing a better understanding of political and societal concerns. But such a goal may be stymied if reporters lack a sufficient understanding of the effect gendered language may have on the conveyance and interpretation of news. To address this issue, we use the Gramulator to conduct an applied natural language processing study of the linguistic and topical features of gendered language in news reportage. Our goal is to offer some insights as to how the choice of language and topics might affect the efficacy of news reportage. Results suggest that current news reportage largely conforms to an established gender divide: Specifically, we find evidence that male reportage is more quantitative and likely to focus on topics such as politics, crime, and the military. By contrast, female reportage is more qualitative, and likely to focus on issues such as home and education. The study is of interest to all current affairs writers (e.g., journalists) because it offers a systematic approach to identifying and assessing the linguistic and topical differences that contribute to gendered language in new reportage.

This study compares automated scoring increases and linguistic changes for student writers in two groups: a group that used an intelligent tutoring system embedded with an automated writing evaluation component (Writing Pal) and a group that used only the automated writing evaluation component. The primary goal is to examine automated scoring differences in both groups from pretest to posttest essays to investigate score gains and linguistic development. The study finds that both groups show significant increases in automated writing scores and significant development in lexical, syntactic, cohesion, and rhetorical features. However, the Writing-Pal group shows greater raw frequency gains (i.e., negative v. positive gains).