Observation of Single Top-Quark production with the CDF II Experiment

We present the observation of electroweak single top-quark production using up to 3.2 fb−1 of data collected by the CDF experiment. Lepton plus jets candidate events are classified by four parallel analysis techniques: one likelihood discriminant, one matrix-element discriminant, one decision-tree discriminant, and one neural-network discriminant. These outputs are combined with a super discriminant based on a neural-network analysis in order to improve the expected sensitivity. In conjunction with one neural-network discriminant using a complementary dataset of MET plus jets events with a veto on identified leptons we observe a signal consistent with the standard model but inconsistent with the background-only model by 5.0 standard deviations, with a median expected sensitivity in excess of 5.9 standard deviations

Observation of Single Top-Quark production with the CDF II Experiment

We present the observation of electroweak single top-quark production using up to 3.2 fb−1 of data collected by the CDF experiment. Lepton plus jets candidate events are classified by four parallel analysis techniques: one likelihood discriminant, one matrix-element discriminant, one decision-tree discriminant, and one neural-network discriminant. These outputs are combined with a super discriminant based on a neural-network analysis in order to improve the expected sensitivity. In conjunction with one neural-network discriminant using a complementary dataset of MET plus jets events with a veto on identified leptons we observe a signal consistent with the standard model but inconsistent with the background-only model by 5.0 standard deviations, with a median expected sensitivity in excess of 5.9 standard deviations

The Discriminant Analysis Used by the IRS to Predict Profitable Individual Tax Return Audits

This paper discusses past and current methods the IRS uses to determine which individual income tax returns to audit. The IRS currently uses the discriminant function to give all individual tax returns two scores; one based on whether it should be audited or not and one based on if the return is likely to have unreported income. The discriminant function is determined by the IRS’s National Research Program, which takes a sample of returns and ensures their accuracy. Previously, the function was determined by the IRS’s Taxpayer Compliance Measurement Program. However, this was too burdensome and time consuming for taxpayers. The data mining techniques of decision trees, regression, and neural networks were researched to determine if the IRS should change its method. Unfortunately IRS tax data were not obtainable due to their confidentiality; therefore credit data from a German bank was used to compare discriminant analysis results to the three new methods. All of the methods were run to predict creditworthiness and were compared based on misclassification rates. The neural network had the best classification rate closely followed by regression, the decision tree, and then discriminant analysis. Since this comparison is not based on IRS tax data, no conclusion can be made whether the IRS should change its method or not, but because all methods had very close classification rates, it would be worthwhile for the IRS to look into them

Neural Class-Specific Regression for face verification

Face verification is a problem approached in the literature mainly using nonlinear class-specific subspace learning techniques. While it has been shown that kernel-based Class-Specific Discriminant Analysis is able to provide excellent performance in small- and medium-scale face verification problems, its application in today's large-scale problems is difficult due to its training space and computational requirements. In this paper, generalizing our previous work on kernel-based class-specific discriminant analysis, we show that class-specific subspace learning can be cast as a regression problem. This allows us to derive linear, (reduced) kernel and neural network-based class-specific discriminant analysis methods using efficient batch and/or iterative training schemes, suited for large-scale learning problems. We test the performance of these methods in two datasets describing medium- and large-scale face verification problems.Comment: 9 pages, 4 figure

A Review of Bankruptcy Prediction Studies: 1930-Present

One of the most well-known bankruptcy prediction models was developed by Altman [1968] using multivariate discriminant analysis. Since Altman\u27s model, a multitude of bankruptcy prediction models have flooded the literature. The primary goal of this paper is to summarize and analyze existing research on bankruptcy prediction studies in order to facilitate more productive future research in this area. This paper traces the literature on bankruptcy prediction from the 1930\u27s, when studies focused on the use of simple ratio analysis to predict future bankruptcy, to present. The authors discuss how bankruptcy prediction studies have evolved, highlighting the different methods, number and variety of factors, and specific uses of models. Analysis of 165 bankruptcy prediction studies published from 1965 to present reveals trends in model development. For example, discriminant analysis was the primary method used to develop models in the 1960\u27s and 1970\u27s. Investigation of model type by decade shows that the primary method began to shift to logit analysis and neural networks in the 1980\u27s and 1990\u27s. The number of factors utilized in models is also analyzed by decade, showing that the average has varied over time but remains around 10 overall. Analysis of accuracy of the models suggests that multivariate discriminant analysis and neural networks are the most promising methods for bankruptcy prediction models. The findings also suggest that higher model accuracy is not guaranteed with a greater number of factors. Some models with two factors are just as capable of accurate prediction as models with 21 factors

PERBANDINGAN ALGORITMA NEURAL NETWORK DENGAN LINIER DISCRIMINANT ANALYSIS (LDA) PADA KLASIFIKASI PENYAKIT DIABETES DI RSUD KUDUS

Salah satu penyakit kronis yang banyak diderita oleh penduduk Indonesia adalah Diabetes Melitus (DM), penyakit ini ditandai dengan nilai kadar glukosa dalam darah di atas normal. Penyakit ini termasuk penyakit yang rumit dan mematikan, oleh karena itu dibutuhkan perawatan medis yang kontinu agar resiko terjadinya komplikasi bisa dihindari. Guna  menganalisa pasien pengidap penyakit diabetes sejak dini, Pencatatan terhadap penyakit ini banyak dilakukan agar dapat dilakukan pencegahan. Salah satu yang dilakukan adalah dengan menggunakan teknik klasifikasi data mining. Teknik klasifikasi  digunakan untuk memprediksi pasien mana yang terkena penyakit diabetes dan tidak. Dalam penelitian ini menggunakan Algoritma kasifikasi data mining neural network  dan linier Discriminant Analysis (LDA). Hasil penelitian menunjukan akurasi sebesar 90.38% dengan algoritma Linear Discriminant Analysis (LDA) dan akurasi sebesar 95,19% didapat pada saat menggunakan algoritma Neural Network. Algoritma Neural Network menghasilkan akurasi lebih baik daripada algoritma Linear Discriminant Analysis (LDA) dalam klasifikasi penyakit diabetes