Two applications of the LSA machine

We present two applications of a learning algorithm that combines logarithmic simulated annealing with the perceptron algorithm. The implementation of the learning algorithm is called LSA machine and has been successfully applied already to the classification of liver tissue from CT images. We investigate the performance of the LSA machine on two sets of numerical data: The Wisconsin breast cancer diagnosis (WBCD) database and microarray data published by Golub et al. (1999). The WBCD data consist of 683 samples with 9 input values that are divided into 444 benign cases (positive examples) and 239 malignant cases (negative examples). The LSA machine has been trained on 50% and 75% of the entire sample set, and the test has been performed on the remaining samples. In both cases, we obtain a correct classification close to 99% which is comparable to the best results published on WBCD data. The training set of the microarray data consists of I I samples of acute myeloid leukemia (AML) and 27 samples of acute lymphoblastic leukemia (ALL), each of them with 7129 input values (gene-expression data). For the test, 14 AML samples and 20 ALL samples are used. We obtain a single classification error (which is a ALL test sample) on seven genes only, which improves on the results published by Golub et al. (1999) by using the model of self-organising maps. Our result is competitive to the best results published for support vector machines.

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