Multi-Group Data Classification via MILP

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch211 ◽

2011 ◽

pp. 1365-1371

Author(s):

Fadime Üney Yüksektepe

Keyword(s):

Mixed Integer Linear Programming ◽

Learning Strategy ◽

Data Classification ◽

Mixed Integer ◽

Classification Methods ◽

Classification Problems ◽

Science Data ◽

Classification Approach ◽

Group Data ◽

Class Labels

Data classification is a supervised learning strategy that analyzes the organization and categorization of data in distinct classes. Generally, a training set, in which all objects are already associated with known class labels, is used in classification methods. The data classification algorithms work on this set by using input attributes and builds a model to classify new objects. In other words, the algorithm predicts output attribute values. Output attribute of the developed model is categorical (Roiger & Geatz, 2003). There are many applications of data classification in finance, health care, sports, engineering and science. Data classification is an important problem that has applications in a diverse set of areas ranging from finance to bioinformatics (Chen & Han & Yu, 1996; Edelstein, 2003; Jagota, 2000). Majority data classification methods are developed for classifying data into two groups. As multi-group data classification problems are very common but not widely studied, we focus on developing a new multi-group data classification approach based on mixed-integer linear programming.

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Hierarchical Classification Using Binary Data

AI Magazine ◽

10.1609/aimag.v40i2.2846 ◽

2019 ◽

Vol 40 (2) ◽

pp. 59-65

Author(s):

Denali Molitor ◽

Deanna Needell

Keyword(s):

Hierarchical Structure ◽

Binary Data ◽

Hierarchical Classification ◽

Hierarchical Data ◽

Classification Problems ◽

Classification Approach ◽

Hierarchical Relationship ◽

Class Labels ◽

Number Of Classes

In classification problems, especially those that categorize data into a large number of classes, the classes often naturally follow a hierarchical structure. That is, some classes are likely to share similar structures and features. Those characteristics can be captured by considering a hierarchical relationship among the class labels. Motivated by a recent simple classification approach on binary data, we propose a variant that is tailored to efficient classification of hierarchical data. In certain settings, specifically, when some classes are significantly easier to identify than others, we show case computational and accuracy advantages.

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Kernelized Hashcode Representations for Relation Extraction

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33016431 ◽

2019 ◽

Vol 33 ◽

pp. 6431-6440

Author(s):

Sahil Garg ◽

Aram Galstyan ◽

Greg Ver Steeg ◽

Irina Rish ◽

Guillermo Cecchi ◽

...

Keyword(s):

Kernel Methods ◽

State Of The Art ◽

Computational Cost ◽

Relation Extraction ◽

Locality Sensitive Hashing ◽

Classification Methods ◽

Classification Problems ◽

Random Subspaces ◽

Class Labels ◽

Biomedical Relation Extraction

Kernel methods have produced state-of-the-art results for a number of NLP tasks such as relation extraction, but suffer from poor scalability due to the high cost of computing kernel similarities between natural language structures. A recently proposed technique, kernelized locality-sensitive hashing (KLSH), can significantly reduce the computational cost, but is only applicable to classifiers operating on kNN graphs. Here we propose to use random subspaces of KLSH codes for efficiently constructing an explicit representation of NLP structures suitable for general classification methods. Further, we propose an approach for optimizing the KLSH model for classification problems by maximizing an approximation of mutual information between the KLSH codes (feature vectors) and the class labels. We evaluate the proposed approach on biomedical relation extraction datasets, and observe significant and robust improvements in accuracy w.r.t. state-ofthe-art classifiers, along with drastic (orders-of-magnitude) speedup compared to conventional kernel methods.

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