Shirish Krishnaj Shevade

Improvements to Platt's SMO Algorithm for SVM Classifier Design

This article points out an important source of inefficiency in Platts sequential minimal optimization (SMO) algorithm that is caused by the use of a single threshold value. Using clues from the KKT conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO. These modified algorithms perform significantly faster than the original SMO on all benchmark data sets tried.

Improvements to the SMO algorithm for SVM regression

This paper points out an important source of inefficiency in Smola and Schölkopfs sequential minimal optimization (SMO) algorithm for support vector machine (SVM) regression that is caused by the use of a single threshold value. Using clues from the KKT conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO for regression. These modified algorithms perform significantly faster than the original SMO on the datasets tried.

A fast iterative nearest point algorithm for support vector machine classifier design

In this paper we give a new fast iterative algorithm for support vector machine (SVM) classifier design. The basic problem treated is one that does not allow classification violations. The problem is converted to a problem of computing the nearest point between two convex polytopes. The suitability of two classical nearest point algorithms, due to Gilbert, and Mitchell et al., is studied. Ideas from both these algorithms are combined and modified to derive our fast algorithm. For problems which require classification violations to be allowed, the violations are quadratically penalized and an idea due to Cortes and Vapnik and Friess is used to convert it to a problem in which there are no classification violations. Comparative computational evaluation of our algorithm against powerful SVM methods such as Platts sequential minimal optimization shows that our algorithm is very competitive.

A simple and efficient algorithm for gene selection using sparse logistic regression.

MOTIVATION This paper gives a new and efficient algorithm for the sparse logistic regression problem. The proposed algorithm is based on the Gauss-Seidel method and is asymptotically convergent. It is simple and extremely easy to implement; it neither uses any sophisticated mathematical programming software nor needs any matrix operations. It can be applied to a variety of real-world problems like identifying marker genes and building a classifier in the context of cancer diagnosis using microarray data. RESULTS The gene selection method suggested in this paper is demonstrated on two real-world data sets and the results were found to be consistent with the literature. AVAILABILITY The implementation of this algorithm is available at the site http://guppy.mpe.nus.edu.sg/~mpessk/SparseLOGREG.shtml SUPPLEMENTARY INFORMATION Supplementary material is available at the site http://guppy.mpe.nus.edu.sg/~mpessk/SparseLOGREG.shtml

Molecular changes from dysplastic nodule to hepatocellular carcinoma through gene expression profiling

Progression of hepatocellular carcinoma (HCC) is a stepwise process that proceeds from pre‐neoplastic lesions—including low‐grade dysplastic nodules (LGDNs) and high‐grade dysplastic nodules (HGDNs)—to advanced HCC. The molecular changes associated with this progression are unclear, however, and the morphological cues thought to distinguish pre‐neoplastic lesions from well‐differentiated HCC are not universally accepted. To understand the multistep process of hepato‐carcinogenesis at the molecular level, we used oligo‐nucleotide microarrays to investigate the transcription profiles of 50 hepatocellular nodular lesions ranging from LGDNs to primary HCC (Edmondson grades 1‐3). We demonstrated that gene expression profiles can discriminate not only between dysplastic nodules and overt carcinoma but also between different histological grades of HCC via unsupervised hierarchical clustering with 10,376 genes. We identified 3,084 grade‐associated genes, correlated with tumor progression, using one‐way ANOVA and a one‐versus‐all unpooled t test. Functional assignment of these genes revealed discrete expression clusters representing grade‐dependent biological properties of HCC. Using both diagonal linear discriminant analysis and support vector machines, we identified 240 genes that could accurately classify tumors according to histological grade, especially when attempting to discriminate LGDNs, HGDNs, and grade 1 HCC. In conclusion, a clear molecular demarcation between dysplastic nodules and overt HCC exists. The progression from grade 1 through grade 3 HCC is associated with changes in gene expression consistent with plausible functional consequences. Supplementary material for this article can be found on the HEPATOLOGY website (http://www.interscience.wiley.com/jpages/0270‐9139/suppmat/index.html). (HEPATOLOGY 2005;42:809–818.)

A Fast Dual Algorithm for Kernel Logistic Regression

This paper gives a new iterative algorithm for kernel logistic regression. It is based on the solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization algorithm for Support Vector Machines. Asymptotic convergence of the algorithm is proved. Computational experiments show that the algorithm is robust and fast. The algorithmic ideas can also be used to give a fast dual algorithm for solving the optimization problem arising in the inner loop of Gaussian Process classifiers.

SMO algorithm for least-squares SVM formulations

This article extends the well-known SMO algorithm of support vector machines (SVMs) to least-squares SVM formulations that include LS-SVM classification, kernel ridge regression, and a particular form of regularized kernel Fisher discriminant. The algorithm is shown to be asymptotically convergent. It is also extremely easy to implement. Computational experiments show that the algorithm is fast and scales efficiently (quadratically) as a function of the number of examples.

Rough set based incremental clustering of interval data

This paper introduces a novel incremental approach to clustering interval data. The method employs rough set theory to capture the inherent uncertainty involved in cluster analysis. Our experimental results show that it produces meaningful cluster abstractions for interval data at a minimal computational expense.

Rapid and brief communication: Rough support vector clustering

In this paper a novel kernel-based soft clustering method is proposed. This method incorporates rough set theoretic flavour in support vector clustering paradigm to achieve soft clustering. Empirical studies show that this method can find soft clusters having arbitrary shapes.

Multi-category classification by soft-max combination of binary classifiers

In this paper, we propose a multi-category classification method that combines binary classifiers through soft-max function. Posteriori probabilities are also obtained. Both, one-versus-all and one-versus-one classifiers can be used in the combination. Empirical comparison shows that the proposed method is competitive with other implementations of one-versus-all and one-versus-one methods in terms of both classification accuracy and posteriori probability estimate.