Decision Error Rate Research Articles

Categorizing digits and the mental number line.

Following the classical work of Moyer and Landauer (1967), experimental studies investigating the way in which humans process and compare symbolic numerical information regularly used one of two experimental designs. In selection tasks, two numbers are presented, and the task of the participant is to select (for example) the larger one. In classification tasks, a single number is presented, and the participant decides if it is smaller or larger than a predefined standard. Many findings obtained with these paradigms fit in well with the notion of a mental analog representation, or an Approximate Number System (ANS; e.g., Piazza 2010). The ANS is often conceptualized metaphorically as a mental number line, and data from both paradigms are well accounted for by diffusion models based on the stochastic accumulation of noisy partial numerical information over time. The present study investigated a categorization paradigm in which participants decided if a number presented falls into a numerically defined central category. We show that number categorization yields a highly regular, yet considerably more complex pattern of decision times and error rates as compared to the simple monotone relations obtained in traditional selection and classification tasks. We also show that (and how) standard diffusion models of number comparison can be adapted so as to account for mean and standard deviations of all RTs and for error rates in considerable quantitative detail. We conclude that just as traditional number comparison, the more complex process of categorizing numbers conforms well with basic notions of the ANS.

Rough set based semi-supervised feature selection via ensemble selector

Similar to feature selection over completely labeled data, the aim of feature selection over partially labeled data (semi-supervised feature selection) is also to find a feature subset which satisfies the intended constraint. Nevertheless, two difficulties may emerge in the semi-supervised feature selection: (1) labels are incomplete since labeled and unlabeled samples coexist in data; (2) the explanation of the selected feature subset is not clear. Therefore, such two problems will be mainly addressed in our research. Firstly, the unlabeled samples can be predicted through various semi-supervised learning methods. Secondly, the Local Neighborhood Decision Error Rate is proposed to construct multiple fitness functions for evaluating the significance of the candidate feature. Such mechanism not only realizes the ensemble selector in the process of feature selection, but also the qualified feature subset will bring us lower decision errors. Immediately, a heuristic algorithm is re-designed to execute feature selection. Finally, through testing nine different ratios (10%, 20%, …, 90%) of labeled samples in data, the experimental results demonstrate that our approach is superior to previous researches, mainly because: (1) the qualified feature subset derived by our approach can provide better classification performance; (2) the lower time consumption is required in our process of feature selection.

Index Modulation for Cluster-Based Wireless Sensor Networks

Index modulation is applied to cluster-based wireless sensor networks (WSNs) in this study. The aim is to design an efficient decision gathering scheme for target-detection cluster-based WSNs. The proposed scheme implies that only a single node reports all the local decisions from cluster-members toward the central entity. Unlike conventional clustering, the reporting node is not fixed as it varies based on the obtained local decisions. Consequently, the local decisions of all cluster members are divided into two groups. The first one is modulated and the other one determines the index of the reporting node. The decision error rate of the proposed scheme is analyzed and an upper bound is derived. The derived bound is shown to precisely match simulation results at pragmatic signal to noise ratio values. Furthermore, the detection threshold for the global decision is optimized for minimizing the decision error rate, and formulated in a closed-form expression.

A Novel Hard Decision Based Simultaneous Target Tracking and Classification Approach.

Methods dealing with the problem of Joint Tracking and Classification (JTC) are abundant, among which Simultaneous Tracking and Classification (STC) provides a modularized scheme solving tracking and classification subproblems simultaneously. However, there is no explicit hard decision on the class label but only soft decision (class probability) is provided. This does not fit many practical cases, in which a hard decision is urgently needed. To solve this problem, this paper proposes a Hard decision-based STC (HSTC) method. HSTC takes all the decision error rate, timeliness, and estimation error into account. Specifically, for decision, the sequential probability ratio test is adopted due to its nice properties and also the adaptability to our situation. For estimation, by utilizing the two-way information exchange between the tracker and the classifier, we propose flexible three tracking schemes related to decision. The HSTC tracking result is divided into three parts according to the time of making the hard decision. In general, the proposed HSTC method takes advantage of both SPRT and STC. Finally, two illustrative JTC examples with hard decision verify the effectiveness of the the proposed HSTC method. They show that HSTC can meet the demand of the problem, and also has the performance superiority in both decision and estimation.

Fidelity Measurement on Signal Detection Algorithms With Application to AFM Imaging

Many signal processing and decision making algorithms reported in contemporary literature characterize the performance of the proposed methods utilizing decision error rates. However there is significant need for strategies that provide quantitative assessment of the fidelity of decisions made by the algorithms. This paper develops novel techniques utilizing which fidelity measures can be assigned quantitatively on some prevalent signal detection algorithms. The developed fidelity measurement methods with the detection algorithms are employed for topography imaging utilizing dynamic mode atomic force microscope (AFM). The AFM is a versatile metrology tool for interrogating material at the nano-scale. In spite of its remarkable achievements, a key issue that remains largely unaddressed is the assessment of fidelity of the measurement data. The developed paradigm facilitates user specific priority for either detection of sample features with high decision confidence or on not missing detection of true features. The fidelity measures presented here are suitable for real-time implementation. A detailed comparative study is presented to characterize the proposed signal detection algorithms and fidelity measurement techniques under practical AFM applications. Comprehensive simulation and experimental data corroborate the effectiveness of proposed methods.

Neighborhood attribute reduction: a multi-criterion approach

Though attribute reduction defined by neighborhood decision error rate can improve the classification performance of neighborhood classifier via deleting redundant attributes, such reduction does not take the variations of classification results into account. To fill this gap, a multi-criterion based attribute reduction is proposed, which considers both neighborhood decision error rate and neighborhood decision consistency. The neighborhood decision consistency is used to measure the variations of classification results if attributes change. Following the novel attribute reduction, a heuristic algorithm is also designed to derive reduct which aims to obtain less error rate and higher consistency simultaneously. The experimental results on 10 UCI data sets show that the multi-criterion based reduction can not only improve the decision consistencies without decreasing the classification accuracies significantly, but also bring us more stable reducts. This study suggests new trends concerning criteria and constraints in attribute reduction.

Comparing lexical decision reaction times and error rates for Southern and British English

The English spoken in the southern states of the US as well as the “received pronunciation” of British English are two varieties that the majority of students at the University of Southern California are relatively unfamiliar with. Some of the popular stereotypes surrounding the two groups of speakers, however, diverge: while it has been shown that certain listeners consistently down-grade Southern speakers on positive attributes like intelligence and competence, no such effect is observed for British speakers. This study aimed to investigate the impact of negative stereotypes on lexical decision reaction times and error rates. A list containing high frequency words, low frequency words and non-words was constructed and a trained voice actor recorded the stimuli in both British and Southern English. Participants heard half of stimuli in each variety. Participants tended to incorrectly reject more real words in Southern English than British. These errors occurred in trials with relatively short reaction times. British English stimuli tended to have slower reaction times. The implications of these findings in light of other experiments comparing lexical decision in these and other varieties of English are discussed.

Congruence analysis of geodetic networks – hypothesis tests versus model selection by information criteria

Abstract Geodetic deformation analysis can be interpreted as a model selection problem. The null model indicates that no deformation has occurred. It is opposed to a number of alternative models, which stipulate different deformation patterns. A common way to select the right model is the usage of a statistical hypothesis test. However, since we have to test a series of deformation patterns, this must be a multiple test. As an alternative solution for the test problem, we propose the p-value approach. Another approach arises from information theory. Here, the Akaike information criterion (AIC) or some alternative is used to select an appropriate model for a given set of observations. Both approaches are discussed and applied to two test scenarios: A synthetic levelling network and the Delft test data set. It is demonstrated that they work but behave differently, sometimes even producing different results. Hypothesis tests are well-established in geodesy, but may suffer from an unfavourable choice of the decision error rates. The multiple test also suffers from statistical dependencies between the test statistics, which are neglected. Both problems are overcome by applying information criterions like AIC.

A novel adaptive differential evolution SVM model for predicting coal and gas outbursts

Parameter selection is a key factor affecting the performance of support vector machines (SVMs). To further improve the classification accuracy and generalization ability of SVMs, a parameter selection model for SVMs with RBF kernel is proposed based on adaptive differential evolution (ADE) algorithm, and is applied to predict coal and gas outbursts. The function of each parameter and its adjustment scheme of differential evolution (DE) algorithm are analyzed, and the algorithm is improved by using the decision error rate of samples as the objective function. Adaptive calculation equations for variability factor and crossover factor are designed. The variability and crossover factors are automatically adjusted in the execution process of the algorithm, so that the population diversity is maintained in the early stages of algorithm execution to enhance the ability of searching global optimal values, while the stability of the algorithm is guaranteed in the late stages by promoting the searching ability for local optimal values. A novel ADESVM model for predicting coal and gas outbursts is established by using ADE algorithm to select SVM parameters, which is applied to predict the coal and gas outbursts. Experimental results show that the designed ADE algorithm has high convergence speed and high computational accuracy. The proposed ADESVM model has higher training speed and is more robust compared with other similar SVM models. It also has higher prediction accuracy and shorter training time, compared with back propagation neural networks, providing a new method for the intelligent prediction of coal and gas outbursts.

Syllable Frequency and Spoken Word Recognition: An Inhibitory Effect.

Research has shown that syllables play a relevant role in lexical access in Spanish, a shallow language with a transparent syllabic structure. Syllable frequency has been shown to have an inhibitory effect on visual word recognition in Spanish. However, no study has examined the syllable frequency effect on spoken word recognition. The present study tested the effect of the frequency of the first syllable on recognition of spoken Spanish words. A sample of 45 young adults (33 women, 12 men; M = 20.4, SD = 2.8; college students) performed an auditory lexical decision on 128 Spanish disyllabic words and 128 disyllabic nonwords. Words were selected so that lexical and first syllable frequency were manipulated in a within-subject 2 × 2 design, and six additional independent variables were controlled: token positional frequency of the second syllable, number of phonemes, position of lexical stress, number of phonological neighbors, number of phonological neighbors that have higher frequencies than the word, and acoustical durations measured in milliseconds. Decision latencies and error rates were submitted to linear mixed models analysis. Results showed a typical facilitatory effect of the lexical frequency and, importantly, an inhibitory effect of the first syllable frequency on reaction times and error rates.

The effect of exercise intensity on cognitive performance during short duration treadmill running.

This study examined the effect of short duration, moderate and high-intensity exercise on a Go/NoGo task. Fifteen, habitually active (9 females and 6 males aged 28 ± 5 years) agreed to participate in the study and cognitive performance was measured in three sessions lasting 10 min each, performed at three different exercise intensities: rest, moderate and high. Results indicated significant exercise intensity main effects for reaction time (RT) (p = 0.01), the omission error rate (p = 0.027) and the decision error rate (p = 0.011), with significantly longer RTs during high intensity exercise compared to moderate intensity exercise (p = 0.039) and rest (p = 0.023). Mean ± SE of RT (ms) was 395.8 ± 9.1, 396.3 ± 9.1 and 433.5 ± 16.1 for rest, moderate and high intensity exercise, respectively. This pattern was replicated for the error rate with a significantly higher omission error and decision error rate during high intensity exercise compared to moderate intensity exercise (p = 0.003) and rest (p = 0.001). Mean ± SE of omission errors (%) was 0.88 ± 0.23, 0.8 ± 0.23 and 1.8 ± 0.46% for rest, moderate and high intensity exercise, respectively. Likewise, mean ± SE of decision errors (%) was 0.73 ± 0.24, 0.73 ± 0.21 and 1.8 ± 0.31 for rest, moderate and high intensity exercise, respectively. The present study’s results suggest that 10 min workout at high intensity impairs RT performances in habitually active adults compared to rest or moderate intensity exercise.

THE INFLUENCE OF SYLLABIFICATION RULES IN L1 ON L2 WORD RECOGNITION.

Experiments with Korean learners of English and English monolinguals were conducted to examine whether knowledge of syllabification in the native language (Korean) affects the recognition of printed words in the non-native language (English). Another purpose of this study was to test whether syllables are the processing unit in Korean visual word recognition. In Experiment 1, 26 native Korean speakers and 19 native English speakers participated. In Experiment 2, 40 native Korean speakers participated. In two experiments, syllable length was manipulated based on the Korean syllabification rule and the participants performed a lexical decision task. Analyses of variance were performed for the lexical decision latencies and error rates in two experiments. The results from Korean learners of English showed that two-syllable words based on the Korean syllabification rule were recognized faster as words than various types of three-syllable words, suggesting that Korean learners of English exploited their L1 phonological knowledge in recognizing English words. The results of the current study also support the idea that syllables are a processing unit of Korean visual word recognition.

The role of orthographic syllable frequency in the syllable frequency effect: evidence from Korean.

There have been ongoing debates as to whether the syllable frequency effect is dependent purely on phonological representation or is also affected by the orthographic representation of the syllable. In two experiments, the authors investigated the effect of the phonologic and orthographic frequencies on the syllable frequency effect by manipulating the first syllable's phonologic (high vs low) and orthographic (high vs low) frequency. Analyses of variance were performed for the lexical decision latencies and error rates in two experiments. Overall, the results of the current experiments showed effects of phonological syllable frequency on lexical decision latencies. Additionally, the results of the current experiments also showed the influence of the orthographic syllable frequency in the form of an interaction between the phonologic and orthographic frequencies. Longer latencies and higher error rates for higher phonological syllable frequency occurred only when the target word had low orthographic syllable frequency. The current study suggests that phonological syllable frequency is the primary sources of the syllable frequency effect while it also suggests that the orthographic representation of the syllable influences the syllable frequency effect as well.

An overview of the CATE algorithms for real-time pitch determination

In this paper, we present a recent algorithm for pitch detection based on an implicit circular autocorrelation of the glottal excitation signal. This algorithm operates in real time without the use of any post-processing technique. This article focuses on the correction of the pitch contours estimated and on the reduction in classification errors in speech signals using simple voicing decision techniques. To evaluate the performance of our algorithms, we used the Bagshaw and Keele databases. We show in this study that the sum of the percentage of the unvoiced errors and the percentage of the voiced errors, for the male Bagshaw corpus, reaches a very good score of 14.67. For the female corpus, our results are also competitive compared to other algorithms using the same database. Concerning the Keele database, we succeed to obtain very good gross pitch error, voicing decision error and F0 frame error rates, respectively, 0.44, 0.65 and 1.55 % in the whole corpus.

Performance analysis of decision directed maximum likelihood MIMO channel tracking algorithm

SUMMARYIn this paper, the performance of decision directed (DD) maximum likelihood (ML) channel tracking algorithm is analyzed. The ML channel tracking algorithm presents efficient performance especially in the decision directed mode of the operation. In this paper, after introducing the method for analysis of DD algorithms, the performance of ML MIMO channel tracking algorithm in the DD mode of operation is analyzed. In this method channel tracking error is evaluated for a given decision error rate. Then, the decision error rate is approximated for a given channel tracking error. By solving these two derived equations jointly, both the decision error rate and the channel tracking error are computed. The presented analysis is compared with simulation results for different channel ranks, Doppler frequency shifts, and signal‐to‐noise ratios, and it is shown that the analysis is a good match for simulation results especially in high rank MIMO channels and high Doppler shifts. Copyright © 2012 John Wiley & Sons, Ltd.

Estimating the designated use attainment decision error rates of US Environmental Protection Agency's proposed numeric total phosphorus criteria for Florida, USA, colored lakes

The utility of numeric nutrient criteria established for certain surface waters is likely to be affected by the uncertainty that exists in the presence of a causal link between nutrient stressor variables and designated use-related biological responses in those waters. This uncertainty can be difficult to characterize, interpret, and communicate to a broad audience of environmental stakeholders. The US Environmental Protection Agency (USEPA) has developed a systematic planning process to support a variety of environmental decisions, but this process is not generally applied to the development of national or state-level numeric nutrient criteria. This article describes a method for implementing such an approach and uses it to evaluate the numeric total P criteria recently proposed by USEPA for colored lakes in Florida, USA. An empirical, log-linear relationship between geometric mean concentrations of total P (a potential stressor variable) and chlorophyll a (a nutrient-related response variable) in these lakes-that is assumed to be causal in nature-forms the basis for the analysis. The use of the geometric mean total P concentration of a lake to correctly indicate designated use status, defined in terms of a 20 µg/L geometric mean chlorophyll a threshold, is evaluated. Rates of decision errors analogous to the Type I and Type II error rates familiar in hypothesis testing, and a 3rd error rate, E(ni) , referred to as the nutrient criterion-based impairment error rate, are estimated. The results show that USEPA's proposed "baseline" and "modified" nutrient criteria approach, in which data on both total P and chlorophyll a may be considered in establishing numeric nutrient criteria for a given lake within a specified range, provides a means for balancing and minimizing designated use attainment decision errors.

Selecting Discrete and Continuous Features Based on Neighborhood Decision Error Minimization

Feature selection plays an important role in pattern recognition and machine learning. Feature evaluation and classification complexity estimation arise as key issues in the construction of selection algorithms. To estimate classification complexity in different feature subspaces, a novel feature evaluation measure, called the neighborhood decision error rate (NDER), is proposed, which is applicable to both categorical and numerical features. We first introduce a neighborhood rough-set model to divide the sample set into decision positive regions and decision boundary regions. Then, the samples that fall within decision boundary regions are further grouped into recognizable and misclassified subsets based on class probabilities that occur in neighborhoods. The percentage of misclassified samples is viewed as the estimate of classification complexity of the corresponding feature subspaces. We present a forward greedy strategy for searching the feature subset, which minimizes the NDER and, correspondingly, minimizes the classification complexity of the selected feature subset. Both theoretical and experimental comparison with other feature selection algorithms shows that the proposed algorithm is effective for discrete and continuous features, as well as their mixture.

Effect Of Hypohydration And Intermittent Running In The Heat On Field Hockey Skill Performance

The development of hyperthermia during intermittent running in the heat results in a decline in skill performance. If players begin a match in a state of hypohydration, the thermoregulatory system is stressed further. However, there is limited research into the influence of hypohydration on skill performance during sports that are multifaceted in their demands, such as field hockey. PURPOSE: To determine the influence of moderate hypohydration on field hockey skill performance following simulated field hockey exercise in the heat. METHODS: Seven elite female field hockey players completed two 70-min field hockey-specific intermittent treadmill running trials (FHITP), separated by ∼7 days, in a randomised crossover design. The evening preceding each trial, participants performed a baseline skills tests before dehydrating via passive hyperthermia to elicit 2% dehydration. Subjects then either immediately rehydrated (EUH) or remained dehydrated (DEH). The next morning subjects completed a skills test pre- and post performing the FHITP in the heat (33°C, 60% rh). Hydration status was maintained with ad libitum water intake. Heart rate (HR), rectal temperature (Trec), rating of perceived exertion (RPE) and thirst sensation (TS) were measured during exercise. Total Time (TT), Decision Time (DT) and Penalty Time (PT) were recorded during the skills test. RESULTS: PT was higher post-FHITP in the DEY trial (EUH: 8.4 ± 3.4 vs. DEY: 14.6 ± 6.1 s; P<0.05). TT and DT were slower post-FHITP, with no difference between trials (P=0.02 and P=0.01, respectively). DT was faster in the EUH trial compared to DEH (EUH; 4.06 ± 0.20 vs. DEY: 4.18 ± 0.23 s; P=0.01). There was a significant difference in body mass loss between trials at the start on the FHITP (DEH: 1.8 ± 0.6% vs. EUH: 0.1 ± 0.4%; P<0.05) and at the end of the FHIPT (DEH: 2.1 ± 0.5% vs. EUH: 0.2 ± 0.6%; P<0.05). Trec increased over time (P<0.01) with a concomitant increase in HR in the 2nd half (P<0.01), with no difference between trials. No difference in RPE or TS was evident (P>0.05). CONCLUSIONS: Mild hypohydration may increase decision time and error rate without any detrimental effects to physiological parameters. Furthermore, field hockey specific intermittent running in the heat decreases skill performance, which may be linked to increased thermoregulatory strain.

Likelihood Estimation for Reduced-Complexity ML Detectors in a MIMO Spatial-Multiplexing System

This paper proposes a likelihood estimation method for reduced-complexity maximum-likelihood (ML) detectors in a multiple-input multiple-output (MIMO) spatial-multiplexing (SM) system. Reduced-complexity ML detectors, e. g., Sphere Decoder (SD) and QR decomposition (QRD)-M algorithm, are very promising as MIMO detectors because they can estimate the ML or a quasi-ML symbol with very low computational complexity. However, they may lose likelihood information about signal vectors having the opposite bit to the hard decision and bit error rate performance of the reduced-complexity ML detectors are inferior to that of the ML detector when soft-decision decoding is employed. This paper proposes a simple estimation method of the lost likelihood information suitable for the reduced-complexity ML detectors. The proposed likelihood estimation method is applicable to any reduced-complexity ML detectors and produces accurate soft-decision bits. Computer simulation confirms that the proposed method provides excellent decoding performance, keeping the advantage of low computational cost of the reduced-complexity ML detectors.

계층변조 시스템에서 신호의 우선순위를 이용한 블라인드 등화

이 논문에서는 계층변조를 적용하는 AT-DMB (advanced terrestrial digital multimedia broadcasting) 시스템에 적용할 수 있는 블라인드등화 기법을 제시하고 그 성능을 살펴본다. AT-DMB 시스템은 기존 T-DMB의 DQPSK (differential quadrature phase-shift keying) 신호와 역방향 호환성을 보장하고 더 높은 데이터 전송률을 지원하기 위해 16-QAM (quadrature amplitude modulation) 계층변조 방식을 적용한다. 이 결과 SNR (signal to noise ratio)에 따라 기존 T-DMB 심볼과 추가된 심볼은 서로 다른 판정오율 (decision error rate)을 가진다. 따라서 판정한 신호로부터 채널을 추정할 때 판정오율이 낮은 선순위 심볼과 판정오율이 높은 후순위 심볼의 신뢰도에 따라 다른 가중치를 줌으로써 판정치의 전체적인 신뢰도를 향상시킬 수 있다. 이 논문에서는 계층변조에 따른 비트 오율(bit error rate) 성능의 악화정도를 분석하고, 모의실험을 통해 판정신호의 신뢰도에 따라 가중치를 주는 제안방식이 가중치를 주지 않은 경우에 비해 비트 오율 성능이 향상됨을 확인하였다. In this paper, we propose a blind equalization method for advanced terrestrial digital multimedia broadcasting (AT-DMB) systems which use hierarchical modulation. The AT-DMB system adopts hierarchical 16-ary quadrature amplitude modulation (16-QAM) to ensure backward-compatibly with the differential quadrature phase shift keying (DQPSK) signal of the legacy terrestrial digital multimedia broadcasting (T-DMB) systems and to support higher transmission rate. Due to the hierarchical modulation, the conventional T-DMB signal and the additional signal have different error rate at same signal to noise ratio (SNR). By weighting the decided symbols differently according to the reliability of the symbols, i.e., high priority symbol with low error rate and low priority symbol with high error rate, we can improve the channel estimation accuracy. In this paper, we analyze SNR loss by hierarchical modulation and confirm it through simulations. Moreover, through simulations, we verify that the proposed weighting method improve BER compared to the no-weighting method.