In ensemble methods the aggregation of multiple unstable classifiers often leads to reduce the misclassification rates substantially in many applications and benchmark classification problems. We propose here a new ensemble, “Double SVMBagging”, which is a variant of double bagging. In this ensemble method we used the support vector machine as the additional classifiers, built on the out-of-bag...

Credit scoring is an effective tool for banks and lending companies to manage the potential credit risk of borrowers. Machine learning algorithms have made grand progress in automatic accurate discrimination good bad Notably, ensemble approaches are a group powerful tools enhance performance scoring. Random forest (RF) Gradient Boosting Decision Tree (GBDT) become mainstream methods precise RF ...

As it may be difficult for users to distinguish a passing execution from a failing execution for a released software system, researchers have proposed to apply the Random Forest algorithm to classify remotely-collected program execution data. In general, execution-data classification can be viewed as a machine-learning problem, in which a trained learner needs to classify whether each execution...

The goal of this project is to use machine learning and computer vision techniques to identify an object in an image. More specifically, given an image of a bird, we would like to identify what species of bird it is. To do this, we have implemented a two step process; first identify the characteristics of the bird in the image, then use those characteristics to predict what species of bird it i...

We present a method, called equivalence learning, which applies a two-class classification approach to object-pairs defined within a multi-class scenario. The underlying idea is that instead of classifying objects into their respective classes, we classify object pairs either as equivalent (belonging to the same class) or non-equivalent (belonging to different classes). The method is based on a...

Building upon developments in theoretical and applied machine learning, as well as the efforts of various scholars including Guimerà and Sales-Pardo (2011), Ruger et al. (2004), and Martin et al. (2004), we construct a model designed to predict the voting behavior of the Supreme Court of the United States. Using the extremely randomized tree method first proposed in Geurts et al. (2006), a meth...

The proliferation of Malware on computer communication systems posed great security challenges to confidential data stored and other valuable substances across the globe. There have been several attempts in curbing menace using a signature-based approach recent times, machine learning techniques extensively explored. This paper proposes framework combining exploit both feature selections based ...

In this paper, we analyze the quality of several commercial tools for sentiment detection. All tools are tested on nearly 30,000 short texts from various sources, such as tweets, news, reviews etc. The best commercial tools have average accuracy of 60%. We then apply machine learning techniques (Random Forests) to combine all tools, and show that this results in a meta-classifier that improves ...

In this paper, we present recent contributions for the battle against one of the main problems faced by search engines: the spamdexing or web spamming. They are malicious techniques used in web pages with the purpose of circumvent the search engines in order to achieve good visibility in search results. To better understand the problem and finding the best setup and methods to avoid such virtua...