In this work, we first attempt to replicate an earlier study on gene selection and clustering, and then we extend this work by applying a different type of hierarchical clustering to dis- cover interesting subsets of genes from breast cancer data. Replication of such studies is a known challenge and an ac- tive area of research in bioinformatics. The work presented in this paper is three-fold. First, we replicate a study conducted at the University of North Carolina to generate an initial set of genes. Second, we apply an approach called Distance Weighted Discrimination to fuse multiple, disparate breast cancer datasets into a single validation set. Third, we per- form hierarchical clustering and k-means clustering on this validation set to discover natural groupings and compare the clusters generated by both methods. While applying the hi- erarchical clustering is part of the reproduction step, we ex- tend the research by trying two different forms of hierarchi- cal clustering. We also apply k-means clustering for the same purpose and compare all three methods using Kaplan-Meier estimation and Cox proportional hazards regression. We dis- cover that among the three methods, k-means clustering gives us the best results.

Watching movies is one of the most popular entertainments among people. Every year, a huge amount of money goes to the movie industry to release movies to the market. In this paper, we propose a multimodal model to predict the likability of movies using textual, visual and product features. With the help of these features, we capture different aspects of movies and feed them as inputs to binary and multi-class classification and regression models to predict IMDB rating of movies at early steps of production. We also propose our own dataset consisting of about 15000 movie subtitles along with their metadata and poster images. We achieve 76% and 63% weighted F-score for binary and multiclass classification respectively, and 0.7 mean square error for the regression model.

Classic k-Nearest Neighbor (kNN) algorithms approximate a regression or classification function at a query point based on the k-nearest training observations. In real-world datasets, however, the set of k neighbors is frequently not uniformly distributed around a given query point. This can result in a locally biased estimate and thus in degraded regression or classification results. This paper presents two new kNN algorithms that adjust the weight of the k-nearest neighbors to achieve a more balanced distribution. Experiments on real-world datasets and a range of synthetic training distributions and noise levels identify conditions under which the algorithms can improve accuracy with minimal increase in computation time.

A crucial point for ensemble learning systems is the capacity of making different errors on any given sample, which highlights the importance of diversity for ensemble-based decision systems. A usual way of increasing diversity is to combine traditional ensemble methods. Based on this context, we propose a novel combining-based algorithm of pool generation using a merging of bagging, random patches, and boosting techniques for ensemble regression problems. Numerical results indicate that, depending on both the dataset and the diversity measurement, our proposal generates a pool of regressors with more diversity when compared to single ensemble generator approaches.

A denial-of-service (DoS) attack is a malicious act with the goal of interrupting the access to a computer network. The result of DoS attack can cause the computers on the network to squander their resources to serve illegitimate requests that result in a disruption of the network’s services to legitimate users. With a sophisticated DoS attack, it becomes difficult to distinguish malicious requests from legitimate requests. Since a network layer DoS attack can cause interruptions to a network while causing collateral damage, it is vital to understand the measures to mitigate against such attacks. Generally, approaches that implement distribution charts based on statistical analysis or honeypots have been applied to detect a DoS attack. However, this is usually too late, as the damage is already done. We hypothesize in this work that a graph-based approach can provide the capability to identify a DoS attack at its inception. A graph-based approach will also allow us to not only focus on anomalies within an entity (like a computer) but also allow us to analyze the anomalies that exist in an entity’s relationship with other entities, thus providing a rich source of contextual analysis. We demonstrate our proposed approach using a publicly-available dataset.

We examine the ability of a genetic algorithm to learn a predictive model that can estimate the likelihood that a physical therapist will receive annual Medicare payments above or below the industry median based on the physical therapist's practice parameters. We compare the performance of a canonical genetic algorithm and a self adaptive genetic algorithm with the performance of traditional logistic regression. Results show that both genetic algorithm approaches are competitive with logistic regression with the canonical genetic algorithm consistently outperforming logistic regression.

Analysis of spontaneous speech is an important tool for clinical linguists to diagnose various dementia types that affect the language processing areas. Prosody is affected by some dementia types, most notably Parkinson's disease (PD, degradation of voice quality, unstable pitch), Alzheimer's disease (AD, monotonic pitch), and the non-fluent type of Primary Progressive Aphasia (PPA-NF, hesitant, non-fluent speech). Prosodic features can be computed efficiently by software. In this study, we evaluate the performance of a SVM classifier that is trained on prosodic features only. The limitation to only prosody yields baseline results that can be used in a later stage to evaluate the added effect of variables of (morpho) syntax. The goal is to distinguish different dementia types based on the recorded speech. Results show that the classifier can distinguish some dementia types (PPA-NF, AD), but not others (PD, PPA-SD).

This paper deals with extracting and learning patterns of assonance as a stylistic feature for author attribution of historical texts. We describe an assonance extraction algorithm, and consider results from an extensive set of machine learning experiments, based on a historical corpus of 18th century American and British texts. The results are compared with those obtained from the use of other prosodic and traditional stylistic features.

Many software systems run on long-lifespan platforms that operate in diverse and dynamic environments. If these software systems could automatically adapt to hardware changes, it would significantly reduce the maintenance cost and enable rapid upgrade. In this paper, we study the problem of how to automatically adapt to sensor changes, as an important step towards building such long-lived, survivable software systems. We address the adaptation scenarios where a set of sensors are replaced by new sensors. Our approach reconstructs sensor values of replaced sensors by preserving distributions of sensor values before and after the sensor change, thereby not warranting a change in higher-layer software. Compared to existing work, our approach has the following advantages: a) exploiting new sensors without requiring an overlapping period of time between new sensors and old ones; b) providing an estimation of adaptation quality; c) scaling to a large number of sensors. Experiments on weather data and Unmanned Undersea Vehicle (UUV) data demonstrate that our approach can automatically adapt to sensor changes with higher accuracy compared to baseline methods.

Taxi demand prediction has recently attracted increasing research interest due to its huge potential application in large-scale intelligent transportation systems. However, most of the previous methods only considered the taxi demand prediction in origin regions, but neglected the modeling of the specific situation of the destination passengers. We believe it is suboptimal to preallocate the taxi into each region based solely on the taxi origin demand. In this paper, we present a challenging and worth-exploring task, called taxi origin-destination demand prediction, which aims at predicting the taxi demand between all region pairs in a future time interval. Its main challenges come from how to effectively capture the diverse contextual information to learn the demand patterns. We address this problem with a novel Contextualized Spatial-Temporal Network (CSTN), which consists of three components for the modeling of local spatial context (LSC), temporal evolution context (TEC) and global correlation context (GCC) respectively. Firstly, an LSC module utilizes two convolution neural networks to learn the local spatial dependencies of taxi demand respectively from the origin view and the destination view. Secondly, a TEC module incorporates both the local spatial features of taxi demand and the meteorological information to a Convolutional Long Short-term Memory Network (ConvLSTM) for the analysis of taxi demand evolution. Finally, a GCC module is applied to model the correlation between all regions by computing a global correlation feature as a weighted sum of all regional features, with the weights being calculated as the similarity between the corresponding region pairs. Extensive experiments and evaluations on a large-scale dataset well demonstrate the superiority of our CSTN over other compared methods for taxi origin-destination demand prediction.