We approach the task of object discrimination as that of learning efficient "codes" for each object class in terms of responses to a set of chosen discriminants. We formulate this approach in an energy minimization framework. The "code" is built incrementally by successively constructing discriminants that focus on pairs of training images of objects that are currently hard to classify. The particular discriminants that we use partition the set of objects of interest into two well-separated groups. We find the optimal discriminant as well as partition by formulating an objective criteria that measures the well-separateness of the partition. We derive an iterative solution that alternates between the solutions for two generalized eigenproblems, one for the discriminant parameters and the other for the indicator variables denoting the partition. We show how the optimization can easily be biased to focus on hard to classify pairs, which enables us to choose new discriminants one by one in a sequential manner. We validate our approach on a challenging face discrimination task using parts as features and show that it compares favorably with the performance of an eigenspace method.

Collaborative models (e.g., wikis) are an increasingly prevalent Web technology. However, the open-access that defines such systems can also be utilized for nefarious purposes. In particular, this paper examines the use of collaborative functionality to add inappropriate hyperlinks to destinations outside the host environment (i.e., link spam). The collaborative encyclopedia, Wikipedia, is the basis for our analysis. Recent research has exposed vulnerabilities in Wikipedia’s link spam mitigation, finding that human editors are latent and dwindling in quantity. To this end, we propose and develop an autonomous classifier for link additions. Such a system presents unique challenges. For example, low barriersto-entry invite a diversity of spam types, not just those with economic motivations. Moreover, issues can arise with how a link is presented (regardless of the destination). In this work, a spam corpus is extracted from over 235,000 link additions to English Wikipedia. From this, 40+ features are codified and analyzed. These indicators are computed using wiki metadata, landing site analysis, and external data sources. The resulting classifier attains 64 % recall at 0.5% false-positives (ROC-AUC = 0.97). Such performance could enable egregious link additions to be blocked automatically with low false-positive rates, while prioritizing the remainder for human inspection. Finally, a live Wikipedia implementation of the technique has been developed. Categories andSubject Descriptors H.5.3 [Group and Organization Interfaces]: collaborative

Ensemble methodology, which builds a classification model by integrating multiple classifiers, can be used for improving prediction performance. Researchers from various disciplines such as statistics, pattern recognition, and machine learning have seriously explored the use of ensemble methodology. This paper presents an updated survey of ensemble methods in classification tasks, while introducing a new taxonomy for characterizing them. The new taxonomy, presented from the algorithm designer’s point of view, is based on five dimensions: inducer, combiner, diversity, size, and members dependency. We also propose several selection criteria, presented from the practitioner’s point of view, for choosing the most suitable ensemble method. Key words:

Data mining is the upcoming research area to solve various problems and classification is one of main problem in the field of data mining. In this paper, we use two classification algorithms J48 (which is java implementation of C4.5 algorithm) and multilayer perceptron alias MLP (which is a modification of the standard linear perceptron) of the Weka interface. It can be used for testing several datasets. The performance of J48 and Multilayer Perceptron have been analysed so as to choose the better algorithm based on the conditions of the datasets. The datasets have been chosen from UCI Machine Learning Repository. Algorithm J48 is based on C4.5 decision based learning and algorithm Multilayer Perceptron uses the multilayer feed forward neural network approach for classification of datasets. When comparing the performance of both algorithms we found Multilayer Perceptron is better algorithm in most of the cases.