Nicolas Cebron

KNIME: The Konstanz Information Miner

The Konstanz Information Miner is a modular environment, which enables easy visual assembly and interactive execution of a data pipeline. It is designed as a teaching, research and collaboration platform, which enables simple integration of new algorithms and tools as well as data manipulation or visualization methods in the form of new modules or nodes. In this paper we describe some of the design aspects of the underlying architecture and briefly sketch how new nodes can be incorporated.

KNIME - the Konstanz information miner: version 2.0 and beyond

The Konstanz Information Miner is a modular environment, which enables easy visual assembly and interactive execution of a data pipeline. It is designed as a teaching, research and collaboration platform, which enables simple integration of new algorithms and tools as well as data manipulation or visualization methods in the form of new modules or nodes. In this paper we describe some of the design aspects of the underlying architecture, briey sketch how new nodes can be incorporated, and highlight some of the new features of version 2.0.

Active learning for object classification: from exploration to exploitation

Classifying large datasets without any a-priori information poses a problem in numerous tasks. Especially in industrial environments, we often encounter diverse measurement devices and sensors that produce huge amounts of data, but we still rely on a human expert to help give the data a meaningful interpretation. As the amount of data that must be manually classified plays a critical role, we need to reduce the number of learning episodes involving human interactions as much as possible. In addition for real world applications it is fundamental to converge in a stable manner to a solution that is close to the optimal solution. We present a new self-controlled exploration/exploitation strategy to select data points to be labeled by a domain expert where the potential of each data point is computed based on a combination of its representativeness and the uncertainty of the classifier. A new Prototype Based Active Learning (PBAC) algorithm for classification is introduced. We compare the results to other active learning approaches on several benchmark datasets.

Adaptive active classification of cell assay images

Classifying large datasets without any a-priori information poses a problem in many tasks. Especially in the field of bioinformatics, often huge unlabeled datasets have to be explored mostly manually by a biology expert. In this work we consider an application that is motivated by the development of high-throughput microscope screening cameras. These devices are able to produce hundreds of thousands of images per day. We propose a new adaptive active classification scheme which establishes ties between the two opposing concepts of unsupervised clustering of the underlying data and the supervised task of classification. Based on Fuzzy c-means clustering and Learning Vector Quantization, the scheme allows for an initial clustering of large datasets and subsequently for the adjustment of the classification based on a small number of carefully chosen examples. Motivated by the concept of active learning, the learner tries to query the most informative examples in the learning process and therefore keeps the costs for supervision at a low level. We compare our approach to Learning Vector Quantization with random selection and Support Vector Machines with Active Learning on several datasets.