Jiang Su

ICML Conference 2011 Conference Paper

Large Scale Text Classification using Semisupervised Multinomial Naive Bayes

• Jiang Su
• Jelber Sayyad Shirab
• Stan Matwin

ICML Conference 2008 Conference Paper

Discriminative parameter learning for Bayesian networks

• Jiang Su
• Harry Zhang
• Charles X. Ling
• Stan Matwin

AAAI Conference 2006 Conference Paper

A Fast Decision Tree Learning Algorithm

• Jiang Su

There is growing interest in scaling up the widely-used decision-tree learning algorithms to very large data sets. Although numerous diverse techniques have been proposed, a fast tree-growing algorithm without substantial decrease in accuracy and substantial increase in space complexity is essential. In this paper, we present a novel, fast decision-tree learning algorithm that is based on a conditional independence assumption. The new algorithm has a time complexity of O(m · n), where m is the size of the training data and n is the number of attributes. This is a significant asymptotic improvement over the time complexity O(m · n2 ) of the standard decision-tree learning algorithm C4. 5, with an additional space increase of only O(n). Experiments show that our algorithm performs competitively with C4. 5 in accuracy on a large number of UCI benchmark data sets, and performs even better and significantly faster than C4. 5 on a large number of text classification data sets. The time complexity of our algorithm is as low as naive Bayes’. Indeed, it is as fast as naive Bayes but outperforms naive Bayes in accuracy according to our experiments. Our algorithm is a core tree-growing algorithm that can be combined with other scaling-up techniques to achieve further speedup.

ICML Conference 2006 Conference Paper

Full Bayesian network classifiers

• Jiang Su
• Harry Zhang

ICML Conference 2005 Conference Paper

Augmenting naive Bayes for ranking

• Harry Zhang
• Liangxiao Jiang
• Jiang Su

AAAI Conference 2005 Conference Paper

Representing Conditional Independence Using Decision Trees

• Jiang Su

While the representation of decision trees is fully expressive theoretically, it has been observed that traditional decision trees has the replication problem. This problem makes decision trees to be large and learnable only when sufficient training data are available. In this paper, we present a new representation model, conditional independence trees (CITrees), to tackle the replication problem from probability perspective. We propose a novel algorithm for learning CITrees. Our experiments show that CITrees outperform naive Bayes (Langley, Iba, & Thomas 1992), C4. 5 (Quinlan 1993), TAN (Friedman, Geiger, & Goldszmidt 1997), and AODE (Webb, Boughton, & Wang 2005) significantly in classification accuracy.